Life Cycle Optimization of Circular Industrial Processes: Advances in By-Product Recovery for Renewable Energy Applications

Abstract

The global shift toward renewable energy and circular economy models requires industrial systems that minimize waste and recover value across entire life cycles. This review synthesizes recent advances in by-product recovery technologies supporting renewable energy and circular industrial processes. Thermal, biological, chemical/electrochemical, and biotechnological routes are analyzed across battery and e-waste recycling, bioenergy, wastewater, and agri-food sectors, with emphasis on integration through Life Cycle Assessment (LCA), techno-economic analysis (TEA), and multi-criteria decision analysis (MCDA) coupled to process simulation, digital twins, and artificial intelligence tools. Policy and economic frameworks, including the European Green Deal and the Critical Raw Materials Act, are examined in relation to technology readiness and environmental performance. Hybrid recovery systems, such as pyro-hydro-bio configurations, enable higher resource efficiency and reduced environmental impact compared with stand-alone routes. Across all technologies, major hotspots include electricity demand, reagent use, gas handling, and concentrate management, while process integration, heat recovery, and realistic substitution credits significantly improve life cycle outcomes. Harmonized LCA-TEA-MCDA frameworks and digitalized optimization emerge as essential tools for scaling sustainable, resource-efficient, and low-impact industrial ecosystems consistent with circular economy and renewable energy objectives.

1. Introduction

The global energy transition is driving a paradigm shift from linear resource consumption toward sustainable and regenerative production models. Conventional industrial systems, based on extract–use–dispose patterns, are no longer viable in the face of resource scarcity, climate change, and increasing environmental degradation [1,2]. In response, the circular economy (CE) framework has gained prominence as a strategy to decouple economic growth from resource depletion by maintaining materials and energy in continuous loops of reuse, recycling, and recovery [3,4]. Within this context, renewable energy systems, such as bioenergy, solar, and wind, play a critical role not only in decarbonizing energy supply but also in enabling the valorization of waste and by-products, thereby closing industrial material cycles [5].

By-product recovery represents one of the most effective pathways for realizing circularity in industrial processes and renewable energy applications [6]. The recovery of nutrients, critical metals, and residual energy from waste streams significantly reduces environmental burdens while decreasing dependence on virgin resources [7,8]. Numerous technologies support this transition, including thermal processes such as pyrolysis and gasification, biological and chemical treatments like anaerobic digestion (AD) and solvent extraction, and emerging biotechnological innovations such as bioleaching or microbial fuel cells [9,10,11]. Recent studies illustrate the expanding implementation of these circular strategies across energy system components, industrial manufacturing, and bio-based processes, where resource recovery and waste minimization are increasingly integrated with energy generation [12,13,14]. Examples include the valorization of process residues and waste materials, the optimization of end-of-life management for renewable energy technologies, and the advancement of eco-design practices for energy-intensive equipment. Together, these initiatives demonstrate the evolution of industrial systems toward self-sustaining, low-impact operations consistent with CE objectives and long-term sustainability goals [15].

Despite significant progress, major challenges remain in scaling up recovery technologies, harmonizing methodological approaches, and integrating life cycle-based sustainability assessments. LCA remains a key instrument for evaluating the environmental performance of industrial systems, allowing for the identification of trade-offs and opportunities for optimization [16,17]. However, variability in data quality, system boundaries, and functional allocation can lead to inconsistencies in comparative studies. To overcome these issues, digital and analytical tools, including process simulation, MCDA, digital twins (DTs), and artificial intelligence (AI), are increasingly employed to couple LCA with dynamic process optimization and decision support [18,19].

At the policy level, initiatives such as the European Green Deal and the Critical Raw Materials Act (CRMA) provide a strategic framework for advancing industrial circularity and resource security [20,21]. These policies reinforce the importance of integrating CE principles into renewable energy supply chains, promoting innovation in by-product recovery, and enabling industrial symbiosis across sectors.

The aim of this review is to synthesize recent advances in by-product recovery within circular industrial systems for renewable energy applications. It examines thermal, biological, chemical, and biotechnological recovery pathways and evaluates how they interface with environmental and policy frameworks. By examining technology performance alongside LCA-based sustainability considerations, the review outlines key opportunities and challenges for system-level circularity.

To ensure comparability across case studies, the discussion is aligned with the European Commission’s Environmental Footprint (EF 3.x) method and recent Joint Research Centre (JRC) rulebooks for photovoltaic (PV) modules and industrial batteries, anchoring the LCA/TEA/MCDA synthesis in methods already applied in EU regulatory contexts.

This review therefore contributes a structured analytical framework rather than a descriptive synthesis. It consolidates four technology families across four strategic sectors under a unified LCA-TEA-MCDA-DT/AI integration model, identifies cross-sector optimization mechanisms, and aligns methodological settings with EF 3.x/JRC rulebooks. This combined conceptual and regulatory framing distinguishes the present work from existing reviews and defines the research gap addressed.

2. Methodology

This review followed a structured and transparent approach combining bibliometric screening and conceptual synthesis. The literature was retrieved from Scopus, Web of Science, and ScienceDirect, complemented by official reports from the European Commission and the International Energy Agency (IEA). The search covered the years 2010–2025.

2.1. Search Strategy and Boolean Strings

To ensure reproducibility, the exact Boolean search strings used in each database are reported below.

Scopus (TITLE-ABS-KEY)

(TITLE-ABS-KEY (“by-product recovery” OR “resource recovery” OR “waste valorization”)) AND (TITLE-ABS-KEY (“LCA” OR “life cycle assessment” OR “TEA” OR “techno-economic” OR “MCDA”)) AND (TITLE-ABS-KEY (“digital twin” OR “process simulation” OR “AI” OR “machine learning”)) AND (TITLE-ABS-KEY (“bioenergy” OR “battery recycling” OR “wastewater” OR “agri-food”)) AND (PUBYEAR > 2009 AND PUBYEAR < 2026). Web of Science (TS = Topic: Title + Abstract + Keywords).

TS = (“by-product recovery” OR “resource recovery” OR “waste valorization”) AND TS = (“LCA” OR “life cycle assessment” OR “TEA” OR “techno-economic” OR “MCDA”) AND TS = (“digital twin” OR “process simulation” OR “AI” OR “machine learning”) AND TS = (“bioenergy” OR “battery recycling” OR “wastewater” OR “agri-food”) AND PY = (2010–2025).

ScienceDirect (TITLE-ABSTRACT-KEYWORDS)

(“by-product recovery” OR “resource recovery” OR “waste valorization”) AND (“LCA” OR “life cycle assessment” OR “TEA” OR “techno-economic” OR “MCDA”) AND (“digital twin” OR “process simulation” OR “AI” OR “machine learning”) AND (“bioenergy” OR “battery recycling” OR “wastewater” OR “agri-food”) AND (2010–2025).

2.2. Screening and Selection

After removing duplicates, 412 papers were initially identified. Following title, abstract, and full-text screening, 116 sources were retained; 106 peer-reviewed studies and 10 policy, standard, or technical documents (ISO 14040 [16]; EU Green Deal; CRMA proposal; EU Batteries Regulation; recent JRCs Reports; EBA Biomethane Map 2025). This screening process is summarized in Figure 1, which presents the PRISMA flow diagram detailing each exclusion step.

The reviewed works were grouped across four technological domains (thermochemical, biological, chemical/electrochemical, and biotechnological routes) and analyzed according to their integration depth with sustainability assessment and digitalization frameworks.

Given that the included studies encompass both experimental and modelling-based designs, methodological quality was assessed using domain-specific criteria such as clarity of functional units (FUs) and system boundaries, transparency of experimental conditions and inventory data, documentation of assumptions, and reporting of uncertainty, rather than relying on a single generalized risk-of-bias instrument.

2.3. Methodological Layers

Dynamic LCA extends conventional static LCA by introducing temporal and operational variability. Environmental indicators such as greenhouse gas emissions (GHG), energy demand, and resource use are updated over time or in real time using process data or time-series information. This method enables adaptive, operation-level evaluation rather than fixed inventories, improving accuracy under evolving industrial or environmental conditions [22].

Artificial intelligence (AI) techniques, such as machine learning, surrogate modeling, and multi-objective optimization, enhance the predictive and prescriptive capability of LCA and process models. They capture non-linear cause–effect relationships, identify environmental and economic hotspots, and support optimization of process parameters for simultaneous cost and impact reduction. This computational framework shifts sustainability assessment from descriptive to predictive and prescriptive analysis [23,24,25].

A DT is a virtual, continuously updated representation of a physical system that connects sensor data, process simulation, and environmental models. In circular industrial applications, it functions as a real-time interface between operations and sustainability performance, enabling impact-aware monitoring, scenario testing, and closed-loop optimization. Coupling DTs with dynamic LCA and AI establishes an integrated, data-driven framework for continuous environmental improvement [26,27].

Together, these methodological approaches and enabling digital technologies form the analytical backbone of this review. Their integration links life cycle data, techno-economic indicators, and decision-support models, providing a comprehensive basis for the assessment and optimization of circular industrial processes within renewable energy systems.

2.4. Gab Analysis

Recent reviews published on LCA-TEA integration in batteries, bioenergy, wastewater treatment, and agri-food systems have generally remained sector-specific and methodologically narrow, most often coupling only two tools (e.g., LCA-TEA or LCA-DT) within isolated technological domains [18,19]. None provide a unified, cross-sector assessment covering four technology families across the four strategic sectors examined here, nor do they integrate LCA, TEA, MCDA, DTs, and AI into a single optimization-oriented framework. Moreover, previous reviews rarely align methodological assumptions with current EU policy rulebooks [26,27]. In contrast, the present review explicitly harmonizes modelling choices with recent JRC EF-consistent methods for PV modules, the EF IT tool for inventory construction, the Carbon Footprint rules for Industrial Batteries (CFB-IND), and the JRC exploratory project ‘Circular Economy Pathway for Renewable Electricity Supply’ (CEPRES), system-level projections. This integrated, policy-aligned scope fills a conceptual and regulatory gap not addressed in the earlier literature and provides a consistent foundation for comparing by-product recovery routes across sectors.

3. Theoretical Foundation for Circular Industrial Optimization

3.1. Circular Economy in Industrial Systems

The shift toward a CE framework represents a systemic transformation of industrial production and consumption patterns. Unlike the linear ‘take–make–dispose’ model, CE aims to regenerate materials and energy across closed-loop systems, minimizing waste and environmental impacts while maintaining product value [28,29]. Central principles of CE include design for durability and reuse, resource efficiency, waste valorization, and industrial symbiosis. Together, these strategies create regenerative systems that emulate natural material cycles, fostering both environmental and economic resilience [15].

Industrial symbiosis, a cornerstone of CE implementation, enables the exchange of materials, energy, and by-products between different facilities or sectors. Waste from one process serves as a raw material for another, reducing resource extraction and emissions [30]. For instance, in renewable energy and related industries, this can involve the reuse of heat, recovery of metals from renewable energy components, or the valorization of biomass residues [12,13,14]. By closing resource loops at multiple scales, circular industrial systems contribute to lower carbon intensity, reduced landfill waste, and enhanced economic stability (Figure 2). To summarize the practical application of circular principles,

Table 1. Core principles of circular economy and examples of implementation in industrial systems.

Circular Economy Principle	Industrial Strategy	Example Application in Renewable Energy/Manufacturing	Expected Outcome
Design for reuse and durability	Modular design, material substitution	Reusable components in wind turbines and PV modules [12,13]	Extended product lifetime, reduced raw material use
Resource efficiency	Energy integration, process optimization	Heat recovery in bioenergy and cement plants [31,32]	Lower energy demand, improved thermal efficiency
Waste valorization	By-product recovery, waste-to-energy conversion	Biomass and sludge conversion to biogas and fertilizers [33,34]	Reduced landfill, renewable energy generation
Industrial symbiosis	Cross-sector resource exchange	Utilization of CO2 from bioenergy for algae cultivation [35]	Reduced emissions, new product streams
Circular supply chains	Reverse logistics, remanufacturing	Battery recycling and metal recovery [36]	Resource security, reduced dependency on virgin ores
Eco-design and regulation	Compliance with EU eco-design directives	Furnaces and ovens designed under CE legislation [14]	Environmental compliance, improved energy ratings

categorizes key CE dimensions and provides representative examples of their use in industrial systems.

3.2. Life Cycle Thinking and Assessment Tools

Life cycle thinking (LCT) provides this integrative perspective, ensuring that improvements in one phase of a product’s life do not generate unintended consequences elsewhere [37].

The most established LCT methodology is the LCA, standardized by ISO 14040 [16] and ISO 14044 [38]. LCA quantifies inputs (energy, materials) and outputs (emissions, waste) throughout the life cycle, from raw material extraction and manufacturing to end-of-life recovery. It typically involves four key stages:

• Goal and scope definition;
• Life cycle inventory analysis;
• Impact assessment;
• Interpretation of results.
• The outcomes guide process optimization, eco-design, and sustainability evaluation in renewable energy systems, industrial processes, and by-product recovery chains [39].

When applied to circular systems, LCA enables comparison of recovery pathways such as pyrolysis, AD, and metal recycling, providing data-driven insights into energy efficiency, emission reduction, and material recovery potential [40]. Its combination with TEA and MCDA facilitates comprehensive evaluation of both environmental and economic performance [41].

Recent advances in digital tools, including DTs and AI, extend LCA beyond static assessment toward dynamic life cycle optimization. DTs simulate real-time process scenarios, while AI improves data quality and predictive accuracy [42]. This integration of computational tools and sustainability metrics represents a major step toward the digitalization of circular industrial systems, enabling adaptive management and continuous improvement. Figure 3 illustrates this framework, showing how digital and analytical technologies combine with LCA to enhance environmental performance and support informed decision-making in circular industrial optimization.

To ensure cross-case comparability, modelling choices are aligned with recent EU harmonization work. In PV, the JRC’s ecodesign-oriented method operationalises EF 3.x and the circular footprint formula (CFF) for PV modules, including the FU in kgCO₂e/kWh delivered, electricity-mix treatments, data quality rating (DQR) rules, and verification, and is supported by a dedicated IT tool enabling consistent inventories. For batteries, the JRC CFB-IND rules implementing Regulation (EU) 2023/1542 [43] are applied, with a FU in kWh over lifetime, CFF-based recycled-content and end-of-life modelling, and verification requirements, allowing hydrometallurgical, pyro-hydro, and bio-hydro routes to be compared on a common EF basis. Beyond environmental indicators, EF-based studies at EU scale demonstrate that LCA can be coupled with environmental life cycle costing (eLCC) and full environmental life cycle costing (feLCC) to incorporate internal and monetised external costs into the decision frame [44,45,46,47]. Because the review spans four sectors with intrinsically different performance metrics, complete harmonization of FUs is neither feasible nor conceptually appropriate. Battery-recycling studies typically adopt FUs such as 1 kg recovered metal or 1 kWh delivered, bioenergy systems use MJ or m³ biomethane produced, wastewater and sludge systems use m³ treated water or kg nutrient recovered, and agri-food valorisation relies on kg feedstock or kg product. To ensure cross-case coherence, FUs definitions were examined for methodological consistency and aligned, where applicable, with EU EF 3.x and JRC rulebooks (e.g., CFB-IND for batteries, EF IT tool conventions, and circularity-relevant modelling guidance). Hotspot interpretation therefore remains sector-specific but methodologically comparable across all case studies.

4. By-Product Recovery Technologies in Renewable Energy Systems

By-product recovery in renewable-energy contexts spans four complementary technology families (thermal, biological, chemical/electrochemical, and biotechnological), each converting heterogeneous residues (biomass, sludges, industrial by-streams, and end-of-life components) into energy carriers (biogas, H₂, syngas, upgraded bio-oils) and materials (hydrochar/char, nutrients, recovered metals, reclaimed water). Thermochemical routes (pyrolysis, gasification, and hydrothermal processing) define operating windows that govern oil/gas/solid fractions and downstream upgrading needs; these determine integration options with refineries and drop-in fuels (e.g., sustainable aviation fuel (SAF) pathways) [48,49]. Biological platforms (AD, fermentations, composting) deliver biogas and nutrient-rich digestates, with reported methane and hydrogen yields highly substrate- and configuration-dependent, motivating co-processing and pretreatment strategies [34,50]. Chemical and electrochemical trains (acid/base leaching, solvent extraction, electrowinning) underpin metal loop-closure from complex residues but hinge on reagent management, impurity control, and energy cleanliness, while biotechnologies (bioleaching, biosorption, microbial electrochemical systems) offer low-temperature selectivity for dilute or mixed streams and can polish broader process chains [10]. Across all families, life cycle modeling choices, such as FU, boundaries, multifunctionality treatment, and inclusion of energy integration and emission controls, critically shape comparative performance, reinforcing the need for transparent, iterative LCA interpretation as these recovery routes scale.

The classification summarized in Figure 4 and

Table 2. Representative operating windows and example applications of major by-product-recovery routes.

Technology Family	Typical Operating Window	Illustrative Example	Main Products/Outcomes	Key References
Thermal	Pyrolysis (400–600 °C, O2-free); Gasification (700–900 °C, O2-limited); HTL (280–370 °C, 10–25 MPa)	Hydrothermal liquefaction of microalgae producing biocrude blended into SAF	Biocrude, syngas, char, upgraded liquid fuels	[51]
Biological	AD (35–55 °C); Fermentation (30–40 °C); Composting (ambient−60 °C)	Co-digestion of sewage sludge + food waste yielding 0.45–0.52 L CH4 g−1 VS	Biogas (CH4 + CO2), digestate fertilizers, bio-ethanol	[34,40]
Chemical/Electrochemical	Acid/base leaching (20–90 °C); Solvent extraction/electrowinning (1–5 V)	Hydrometallurgical recovery of Sc from bauxite residue (≈95% efficiency)	Recovered metals, purified electrolytes	[52]
Biotechnological	Ambient−40 °C; pH 2–5; microbial electrolysis 0.8–1.2 V	Bioleaching of rare earth elements; microbial fuel cells for wastewater H2 recovery	Recovered critical metals, bio-H2, electricity	[10,53]

illustrates how by-product-recovery technologies form complementary pathways within circular industrial systems. Each approach operates at distinct temperature, pressure, and biochemical or electrochemical conditions but collectively targets resource circularity and energy efficiency. The following subsections (Section 4.1, Section 4.2, Section 4.3 and Section 4.4) discuss in greater detail the operating principles, process performances, and life cycle findings for thermal, biological, chemical/electrochemical, and biotechnological routes.

Across all four technology families, reported efficiencies span 40–95% for metal recovery (chemical/biotechnological), 0.45–0.52 L CH₄ g⁻¹ VS for AD-based bioenergy, and 60–70% carbon conversion in thermochemical routes. Technology readiness level (TRL) levels range from 5 to 6 for biotechnological routes to 8 to 9 for AD, hydrometallurgy, and membrane-based systems. These values, together with the hotspot patterns identified by recent LCAs, support the comparative discussion developed in the sectoral case studies.

4.1. Thermal Processes

Scope and pathways. Thermochemical routes convert heterogeneous biomass and organic residues into energy carriers and recoverable materials through controlled heating in the presence (or absence) of oxidants or water. Pyrolysis (oxygen-free) yields bio-oil, syngas, and char; gasification (oxygen-limited) produces CO/H₂-rich syngas for power or fuels; and hydrothermal routes, such as hydrothermal carbonization (HTC), liquefaction (HTL), and gasification (HTG), treat wet feedstocks without energy-intensive drying, producing hydrochar, biocrude, or gas, respectively [54]. These tracks are central to valorizing lignocellulosic residues and wet wastes (algae, sludges) that otherwise face disposal burdens. Reviews of lignocellulosic conversion detail pretreatment sensitivities, product slates, and upgrading levers, noting that structural recalcitrance of cellulose/hemicellulose/lignin still constrains selectivity and yields for target molecules and fuels [48,55]. Thermochemical overviews emphasize that operating windows (temperature, pressure, residence time, and catalyst) govern phase distribution (oil/gas/solid) and downstream compatibility of intermediates.

Energy and material recovery. In pyrolysis, the bio-oil fraction densifies energy for transport or co-processing but requires upgrading (e.g., hydrodeoxygenation or catalytic cracking) to improve stability and heating value; char/hydrochar can be activated for materials or used as soil amendments/reductants; and syngas quality dictates power/ Fischer-Tropsch (FT)-fuels potential [49]. Hydrothermal platforms (HTL ~280–370 °C; 10–25 MPa) produce biocrude that integrates with refinery hydrotreaters; HTC (180–260 °C; 2–6 MPa) yields hydrochar while tolerating high-moisture feed; and HTG (≥400 °C; >25 MPa) under supercritical water conditions elevates gas fractions (H₂, CH₄) [56,57]. From a systems perspective, thermochemical streams are also enablers for aviation fuels, with recognized routes such as oil-to-jet (OTJ), sugar-to-jet (STJ), gas-to-jet (GTJ via gasification/FT), and alcohol-to-jet (ATJ), thereby linking waste valorization to sustainable fuels markets [49,58,59].

Assessment and integration. LCA appraisals of thermochemical recovery repeatedly show that energy-related impacts dominate, making process integration (e.g., sensible heat recovery, hot-gas cleanup) pivotal to improve environmental profiles; conversely, neglecting gas capture/clean-up or assuming unrealistic transport can bias results [60]. Standard LCA guidance (ISO/ILCD) highlights decisions around FU, system boundaries, and multifunctionality (e.g., co-products char/oil/gas), which strongly affect comparative outcomes against biological or chemical recovery [61]. Finally, bio-based waste handling choices (e.g., digestion vs. composting vs. incineration) and their biogenic CH₄/N₂O emissions can swing climate results, underscoring the need to model captured-energy credits and avoided burdens consistently [62].

4.2. Biological Processes

AD and co-processing. AD converts organic matter in sludges, agro-residues, and organic fraction of municipal solid waste (OFMSW) to biogas (CH₄/CO₂), with digestate returning N-P-K to soils [63]. Reported methane and hydrogen yields vary widely with substrate and configuration; for example, compiled data show biohydrogen yields up to 6 mol H₂ per mol hexose for integrated dark fermentation + microbial electrolysis and a broad range for agricultural residues (e.g., rice straw, wheat straw) reflecting pretreatment and inoculum effects [53]. Process intensification via co-digestion and side-stream controls (ammonia stripping; struvite precipitation) addresses inhibition and recovers nutrients, aligning AD with resource-factory wastewater treatment plants (WWTPs) [34,40,50].

Fermentation and composting. Beyond AD, sugar/starch fermentation to ethanol and gas fermentation (CO/CO₂) supply intermediates for fuels/chemicals; composting stabilizes organics and can condition substrates prior to wet biological routes. Reviews note the coupling of pretreatment-fermentation-upgrading in integrated biorefineries targeting aviation-range molecules (e.g., STJ/ATJ), while composting vs. digestion choices must consider biogenic greenhouse gases and nutrient credits in LCA. In aviation contexts, biojet pathways spanning OTJ/STJ/GTJ/ATJ demonstrate how biological and thermal steps can be hybridized to meet SAF specifications [49,58,59].

Environmental performance and system effects. Wastewater LCA syntheses consistently report that energy consumption and gas handling dominate global warming, acidification, and eutrophication outcomes; inclusion of sludge fate, transport distances, and fertilizer substitution is crucial to avoid bias. Method chapters in the LCA handbook detail how uncertainty/sensitivity and proper classification/characterization underpin robust comparisons of AD vs. thermal routes [16,39]. Finally, broad biofuels reviews converge on the point that advanced biofuels and integrated biorefineries can deliver climate benefits if land-use, emissions control, and energy integration are handled rigorously, again reinforcing the value of life cycle optimization.

4.3. Chemical and Electrochemical Processes

Hydrometallurgy and solvent extraction. Acid/base leaching solubilizes target metals from complex matrices (ashes, slags, residues), followed by solvent extraction (SX) for separation and electrowinning (EW) for metal recovery. In circular supply chains, the LCA footprint depends strongly on reagent production, energy mix, and effluent treatment (toxicity/ecotoxicity categories) [37,39,52]. Avoided-burden modelling of virgin metal displacement and electrolyte recycling is essential for consequential interpretations.

Electrochemical recovery and refinery integration. Electrochemical routes (EW, electro-refining, microbial electrolysis for H₂) benefit from clean electricity and process intensification. Thermochemical-chemical hybrids, e.g., upgrading HTL biocrude by hydrotreating/hydrocracking or catalytic deoxygenation, demonstrate how chemical steps elevate fuel quality and compatibility with refinery infrastructure. Linking syngas from gasification to FT synthesis (GTJ) is a canonical chemical conversion that closes the loop from waste biomass to drop-in fuels [56,58].

Hydraulic energy recovery in water-distribution systems. Recent studies highlight hydraulic energy embedded in pressurized water networks as an underexploited recovery opportunity within the chemical/electrochemical family [64,65]. Pressure-reducing valves (PRVs) and break-pressure tanks (BPTs) dissipate mechanical energy that can be converted to electricity through turbines or pumps-as-turbines (PATs). Typical PRVs dissipate ~10 kW on average, with peaks up to 100 kW; real-network data show 0.1–83 kW per site, and installed micro-turbines commonly range 5–100 kW [65]. Replacing PRVs with micro-turbines or PATs enables simultaneous pressure regulation and energy recovery. Integrated water-power optimization frameworks demonstrate that co-parametrizing water-distribution system (WDS) and power-delivery systems (PDS), for instance, combining PV, storage, and PRV-replacement turbines, maximizes renewable energy injection while maintaining hydraulic constraints. Such electro-mechanical recovery aligns with the broader circular industry vision of sustainable energy integration [5,15,30].

Comparative assessment and risk drivers. When chemical recovery is compared with AD/pyrolysis, energy intensity, emissions from reagents/solvents, and toxicity-related categories (human/ecotoxicity) often steer the ranking of options; ISO/ILCD chapters highlight how inventory quality, impact-method selection, and treatment of multifunctionality determine the robustness of conclusions [39]. In waste-management contexts (sludge/biomaterials), capture vs. uncontrolled release of biogenic gases can invert climate results, reminding practitioners to model emissions control and substitution credits consistently across chemical vs. bio/thermal scenarios.

4.4. Biotechnological Processes

Bioleaching and biosorption. Microbial consortia (bacteria/fungi) mobilize metals from low-grade or dilute matrices under mild conditions; biosorbents (e.g., functionalized biomass) capture ions selectively, offering low-energy alternatives or pre-steps to hydrometallurgy [10]. Within broader bioprocess reviews, biotechnology is positioned as a low-impact complement where thermal/chemical routes face selectivity or scale constraints, particularly for dilute wastewaters and mixed waste electronics streams.

Microbial electrochemical systems (MES). Microbial fuel cells (MFCs) and microbial electrolysis cells (MECs) convert chemical energy in wastewater to electricity or H₂, integrating treatment with resource recovery. Compiled performance data show biohydrogen yields spanning orders of magnitude depending on feedstock and configuration (e.g., 6 mol H₂/mol hexose for integrated dark fermentation + MEC; tens to hundreds of mL H₂ L⁻¹ for specific agri-residues), highlighting both potential and variability [53]. In practice, MES often complement AD or chemical steps as polishing/recovery modules in circular WWTPs.

Sustainability and system integration. As with other routes, boundary choices in LCA, credits for recovered metals/nutrients, electricity substitution, and emissions control, are decisive for biotechnology’s comparative profile. Wastewater LCA reviews stress that energy supply, methane capture, and nutrient substitution are pivotal drivers, and uncertainty/sensitivity analysis should be embedded to avoid over-interpreting pilot-scale data [39]. Bioenergy reviews also underline the role of integrated biorefineries and hybrid thermochemical–biological chains to reach scale and product specifications, which is consistent with positioning biotechnology as a targeted, selectivity-first step in circular process trains.

5. Case Studies and Sectoral Applications

Four case studies (battery and e-waste recycling, bioenergy and biofuels, industrial wastewater and sludge treatment, and agri-food and biomass processing) were selected to represent distinct yet complementary domains in which by-product recovery and circularity principles are currently being operationalized at scale. These sectors collectively encompass the four technology families discussed in Section 3, providing a balanced framework to assess technological maturity and cross-sectoral transferability. Each case exemplifies a high-relevance resource loop, such as critical metal recirculation, carbon and nutrient recovery, and valorization of bio-based residues, that contributes directly to the objectives of the European Green Deal and the CRMA. Furthermore, these applications are characterized by robust life cycle and techno-economic datasets, enabling consistent comparison of environmental performance, integration potential, and optimization pathways. The selected case studies therefore serve as representative models for evaluating how circular industrial strategies can advance resource efficiency, emission reduction, and sustainable energy integration across interconnected value chains. System-level projections underline the stakes: the JRC’s CEPRES study estimates ~6.2 Mt of recyclable steel aluminum copper already arising from PV, wind and fossil plant decommissioning (2014–2023), with PV wastes reaching 21–35 Mt by 2050, motivating the recovery and optimization pathways assessed here [44,45].

5.1. Battery and E-Waste Recycling

End-of-life (EoL) lithium-ion batteries (LIBs) and broader electronic waste (e-waste) are rapidly growing secondary resources for critical metals (Li, Co, Ni, Mn, Cu, Al, rare metals), yet remain challenging because of heterogeneous chemistries, safety risks, and pollutant releases during treatment [65,66]. Global e-waste exceeded 53.6 Mt in 2019 and is projected to reach ~74 Mt by 2030, while formal recycling still captures a small fraction, underscoring the urgency of efficient, environmentally robust recovery chains [67]. Inadequate practices can emit toxic gases (e.g., polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDD/Fs) and mobilize heavy metals, highlighting the need for best available techniques and transparent LCA to guide technology choices and policy [68].

Technology landscape and critical metal recovery. Industrial recycling routes cluster into (i) pyrometallurgy (smelting/roasting) (smelting > 1000 °C): mature, high throughput, but traditionally poor at Li retention and energy-intensive, slag recycling or secondary hydromet steps are required to capture Li and Mn; (ii) hydrometallurgy (acid/alkali leaching (H₂SO₄, HCl, NH₄OH) + SX/ion exchange (IX)/precipitation): high purity and flexibility, but reagent- and wastewater-intensive; (iii) direct/‘design for relithiation’ and solvometallurgy: emerging options that may preserve active materials or operate at lower temperatures (ionic liquids, deep eutectic solvent (DES)), with scale-up and cost still evolving [68,69,70]. Comparative reviews consistently report that pyro readily consolidates Co/Ni into alloys but sacrifices Li to slag/dust; hydro can close the Li loop when designed as a stand-alone train; and solvomet routes show promising selectivity/yields at 80–250 °C but face TRL and cost barriers [69,70].

At the policy level, EU Regulation 2023/1542 mandates recycled content and recovery efficiency thresholds for Li (50–80%) and Co/Ni/Cu (90–95%), stimulating industrial investment in hybrid pyro-hydro flowsheets and reinforcing the need for transparent environmental documentation [43].

At the policy level, EU Regulation 2023/1542 establishes stringent sustainability, recycled content, and recovery efficiency requirements for lithium-ion batteries, setting thresholds of 50–80% for lithium and 90–95% for cobalt, nickel, and copper. These measures stimulate industrial investment in advanced hybrid pyro-hydrometallurgical processes, encourage the adoption of hydrometallurgical and solvometallurgical flowsheets capable of efficiently capturing lithium, and reinforce the need for transparent environmental documentation, due diligence, and full life cycle accountability [43]. Because industrial battery carbon footprint classes/thresholds under the EU Battery Regulation hinge on the JRC CFB-IND methodology (FU, boundaries, electricity modelling, CFF, verification), process designs and recovery routes assessed here are aligned with that rule set by default.

At the industrial level, large-scale hydrometallurgical and combined pyro-hydro plants, such as those operated by Li-Cycle, Umicore, and Northvolt, have achieved multi-kiloton capacities, representing the only configurations currently established at commercial scale. In contrast, solvometallurgical and biotechnological recycling routes remain at pilot or demonstration stages. Mechanical and thermal pre-treatments, along with downstream leaching, can release toxic gases, and the rising number of fire and explosion incidents during transport and processing underscores the necessity of safety-by-design strategies, including battery discharge, inerting, and rigorous exhaust treatment, as well as the integration of accident and emission scenarios into risk-aware LCAs.

Biotechnological pathways and industrial readiness. Emerging biotechnological processes offer promising low-impact alternatives for critical metal recovery from LIBs and mixed e-waste. Bioleaching, employing bacteria such as Acidithiobacillus ferrooxidans and fungi such as Aspergillus niger, mobilizes Li, Co, and Ni under mild aqueous conditions (pH 2–3, 30–35 °C) through biological oxidation of Fe²⁺/S⁰ intermediates [66,71]. Recent studies report recovery efficiencies of 85–95% for Co/Ni and 60–80% for Li after 5–10 days [72,73]. Hybrid bio-hydrometallurgical schemes, coupling enzymatic oxidation with chemical leaching, reduce acid consumption by up to 40% and improve selectivity toward Mn and Co salts [74,75]. Biosorption using immobilized microbial or algal biomass further enables recovery of rare-earth and transition metals from dilute effluents [10].

While laboratory and pilot-scale reactors (10–50 L) demonstrate encouraging yields, industrial deployment remains limited due to slower kinetics, electrolyte toxicity, and scale-up uncertainties. The TRL is currently estimated at 4–6, positioning bio-routes as complementary pre- or post-treatments within chemical-electrochemical recovery chains [76]. Comparative LCA shows up to 60% lower greenhouse gas emissions and near-neutral toxicity potential relative to acid leaching, provided that nutrient recycling and effluent control are implemented [69,77]. These approaches illustrate the integration of biotechnology within the chemical/electrochemical family, extending circular recovery to dilute and complex waste matrices.

Environmental and LCA results. Recent LCA and process simulation studies clarify the environmental trade-offs and impact drivers across different lithium-ion battery recycling routes:

• Hydrometallurgy: Major hotspots include acid and base production (notably H₂SO₄, NaOH, and Na₂CO₃) and electricity consumption for drying and solid–liquid separations. Incorporating water reuse and gas-scrubbing systems can lower GHG emissions by 30–40% compared with base scenarios. Simulation-based LCA (EF 3.x) confirms that sulfuric acid use and process emissions dominate impact categories, while outcomes remain highly sensitive to validated reaction kinetics and plant-scale data quality [69].
• Pyrometallurgy: Hotspots are driven by high-temperature utilities and reductant combustion (e.g., coke), resulting in the highest climate burdens among recycling routes. Nevertheless, pyrometallurgical systems achieve robust Co/Ni alloy recovery and lower toxicity indices when effective flue gas cleaning is implemented. Lithium typically partitions to slag or flue dust unless recovered through downstream hydrometallurgical or solvomet steps [69,70].
• Hybrid pyro-hydro systems: Process simulation-based LCAs using the EF 3.x framework show that hybrid configurations mitigate key hotspots by combining throughput efficiency with enhanced lithium recovery, achieving 30–50% lower overall environmental impacts compared with stand-alone pyro routes [69].
• Biotechnological and solvomet routes: Although still pre-commercial, these processes show reduced toxicity and energy intensity. Their environmental performance depends on hotspot control related to nutrient medium reuse, solvent management, and bioreactor energy supply [70].

Across all configurations, system boundaries, allocation rules, and emission control assumptions (e.g., off-gas treatment, water recirculation, and effluent management) determine the robustness of results. When closed-loop substitution is applied, where recovered metals displace virgin Co, Ni, and Li salts, optimized hydrometallurgical and bio-hydro systems demonstrate net-negative resource depletion impacts and superior climate performance relative to pyro-only baselines [70].

Design and optimization insights

• Hybrid pyro-hydro-bio configurations enable near-complete recovery of critical metals, achieving compliance with EU Regulation 2023/1542 performance thresholds while significantly reducing energy use and reagent demand through integrated heat and water management [69].
• Process simulation-based LCA serves as a key tool for early-stage eco-optimization, linking laboratory, pilot, and industrial data to ensure consistent inventories and reliable environmental benchmarks [18,69,74].
• Safety-by-design and effluent control must be embedded into both process engineering and LCA frameworks, as unmitigated emissions, off-gas leakage, and fire risks remain major sustainability and safety challenges in recycling operations [70,74,78].

When implemented within DT environments and simulation-coupled LCA, these measures enable continuous eco-optimization of battery recycling routes under real-time operating conditions.

Industrial maturity and future outlook. Hydrometallurgical recycling systems have reached full industrial maturity (TRL 8–9), operating at commercial scale through established European and Asian facilities that meet the recovery thresholds defined by the EU Batteries Regulation [43,69]. Pyro-hydro hybrid configurations function at semi-commercial scale, combining the high throughput of pyrometallurgy with enhanced Li and Co recovery through downstream hydromet steps, as evidenced by process simulation LCAs and pilot plant demonstrations [69,70]. Solvometallurgical and direct recycling routes are currently advancing through demonstration stages (TRL 6–7), showing potential reductions in reagent use and energy demand but still constrained by solvent recovery and material heterogeneity challenges [70,74]. Biotechnological processes remain at pilot scale (TRL 4–6), exhibiting promising environmental profiles yet limited kinetics and industrial readiness, consistent with bio-hydrometallurgical reviews and pilot data [66,71,76]. Moving forward, the integration of DT modelling, process simulation, and LCA-driven optimization will be essential for validating technical and environmental performance, improving cross-scale data coherence, and enabling circular, low-impact recovery chains for critical materials within renewable energy and e-waste sectors [18,69,74].

For battery systems, the analysis applies the JRC’s 2025 CFB-IND rules implementing Regulation (EU) 2023/1542. These rules define the FU as the kWh delivered over the service life, set explicit electricity modelling requirements, prescribe CFF-based recycled-content and end-of-life modelling, and specify third-party verification criteria. Applying this framework ensures consistent and comparable LCA results when comparing hydrometallurgical, pyro-hydrometallurgical, and bio-hydrometallurgical routes [45,46,47].

5.2. Bioenergy and Biofuels

Bioenergy and biofuels constitute a cornerstone of renewable energy transition strategies by converting organic residues into low-carbon fuels and co-products that underpin circular industrial systems [49,79]. These processes recover carbon, nutrients, and energy from agro-industrial wastes, municipal organics, and process effluents, thereby coupling waste management with sustainable fuel supply. Key by-products, such as digestate, CO₂ from biogas, crude glycerol, and solid residues (ash or hydrochar), form critical loops for nutrient and material recirculation [49]. Current research increasingly targets integrated valorization of these streams, linking CO₂ utilization, digestate nutrient recovery, and biochemical upgrading of glycerol and thermochemical residues to minimize life cycle burdens and enhance renewable energy symbiosis [5,33,35,48,49]. Consistent with the previous case study, LCA and process simulation remain essential tools for quantifying environmental performance and guiding optimization within the framework of the European Green Deal and the Circular Bioeconomy Action Plan.

Technology landscape and key by-product streams. Bioenergy systems integrate biological (AD, fermentation), thermochemical (hydrothermal carbonization/liquefaction-HTC/HTL, gasification), and hybrid routes that transform diverse biomass feedstocks into biogas, syngas, or bio-oils [80]. Each platform offers complementary strengths in feedstock flexibility, energy yield, and by-product generation.

According to the Clean Energy Technology Observatory: Bioenergy in the EU 2023 [81], AD is a commercial, fully established technology (TRL 8–9) with minimal environmental impacts when operated on manure, food waste, or sewage sludge. By the end of 2021, Europe counted 18,774 biogas plants and 1067 biomethane facilities, jointly producing 196 TWh (≈18.4 bcm) of energy. In contrast, biomass combustion for heat and combined heat and power (CHP) is also commercial (TRL 8–9), while gasification and pyrolysis remain at demonstration TRL 6–7, and hydrothermal processes are advancing from pilot TRL 4–6 toward industrial proof of concept [81].

Recent comparative analyses report over 1600 biomethane plants in operation across Europe by 2025 [82], employing technologies such as water or amine scrubbing, pressure swing adsorption (PSA), membrane, and cryogenic separation, while emerging biological upgrading approaches use methanogenic consortia or microalgae to valorize CO₂ with H₂ co-feed [33,83]. These developments reinforce the role of bioenergy as a mature yet continuously evolving pillar of circular renewable systems [84].

Digestate valorization and CO₂ utilization. Digestate, containing 4–8% dry matter nutrients (N-P-K) and organic C, is suitable for agricultural recycling provided that pathogens and contaminants are controlled. LCAs indicate that performance depends on field emissions (NH₃/N₂O), storage and spreading practices, and transport distance; realistic fertilizer substitution credits are essential for unbiased comparisons [39,40,63]. Recent synthesis by Chojnacka and Moustakas [85] highlights that effective digestate management is essential for achieving carbon-neutral bioenergy systems. Advanced strategies, such as nutrient recovery, co-composting, ammonia stripping, and biochar co-processing, can simultaneously improve fertilizer efficiency and lower GHG emissions, aligning with circular bioeconomy objectives [85].

CO₂ streams from biogas upgrading constitute further valorization opportunities:

• Biological methanation, in situ or ex situ, converts CO₂ and H₂ to CH₄ at 40–65 °C via hydrogenotrophic methanogens, achieving 90–98% conversion and improving carbon efficiency [33,79,81].
• Algal carbon capture and utilization (CCU) uses microalgae to fix CO₂ into biomass for fuels, pigments, or feed, closing nutrient loops when cultivated on digestate or wastewater [33,35].

Both routes demonstrate strong circular potential but remain pilot-scale (TRL 6–7), sensitive to energy supply and process control [81].

By-product valorization (glycerol, ash and others). Crude glycerol (≈10 wt% of biodiesel) is increasingly treated as a platform feedstock for secondary biofuels and biochemicals. Recent reviews by Elsayed et al. [86] and Kaur et al. [87] outline complementary biological and thermochemical routes for crude glycerol valorization, including fermentation to ethanol, 1,3-propanediol, or H₂; lipid production by oleaginous yeasts or microalgae; and catalytic reforming to syngas. Both studies highlight that employing impurity-tolerant microorganisms or catalysts can significantly reduce the need for energy-intensive purification by 25–45% compared to conventional refining. Comparative life cycle modeling by Vanapalli et al. [88] further indicates that fermentation-based pathways (e.g., fed-batch conversion to 1,3-propanediol) can outperform hydrogenolysis in greenhouse gas mitigation when coupled with heat recovery and resource recycling. Integrated ‘biocircular’ chains linking glycerol fermentation with wastewater or lignocellulosic residues show favourable LCA and techno-economic profiles. Moreover, solid by-products such as ashes and hydrochars from HTL, HTC, or gasification retain inorganic nutrients (K, Ca, P) that can be recovered as fertilizers or fillers, although LCAs caution that heavy metal mobility and uncertain substitution factors may offset some of these benefits [56,57,60,61,62].

Environmental and LCA results

Biogas upgrading → biomethane: Energy use, solvent makeup, and methane slip dominate climate impacts. Process simulation-based LCAs reveal that integrating heat recovery and water/amine recycling lowers GHG emissions by 30–40% compared with baseline upgrading [40,83].

Digestate application: When fertilizer substitution is credited and field losses modelled, net climate benefit ranges from −50 to +100 kg CO₂-eq t⁻¹ digestate, underlining the influence of management choices [40,63].

Glycerol valorization: Life cycle inventories show that bioconversion routes shift hotspots from chemical purification to reactor energy and nutrient input; integrating digestate-based nutrients and water recirculation can cut GHG by 40–60% relative to stand-alone refining [86].

Thermochemical residues: Hydrothermal and pyrolysis chains exhibit large variance depending on process integration and product use; optimized heat exchange and gas cleanup improve GHG and acidification scores by 20–30% [61,62].

Design and optimization insights

• Energy integration and methane slip control: Selecting upgrading and CO₂-utilization technologies that minimize CH₄ losses (<0.1%) and recover latent heat is critical for climate-neutral operation [83].
• Process simulation-based LCA and DTs: Coupling simulation tools with dynamic LCA enables consistent inventories from lab to industry and identifies eco-optimization levers for biogas and biorefinery chains [18,19].
• Biomimetic and AI-driven design thinking: Sustainable design frameworks inspired by biomimicry and machine learning support integrated AD-methanation-microalgae loops for resource symbiosis [7,89].

Coupling these strategies with DTs sand dynamic LCA allows biomethane and biorefinery systems to be optimized in real time under varying feedstock, energy, and market conditions.

Industrial maturity and future outlook. AD and biomethane upgrading technologies (water, amine, PSA, membrane, cryogenic) are fully industrialized (TRL 8–9) across Europe and Asia, with over 1 billion Nm³ yr⁻¹ biomethane production [83]. Biological methanation is progressing to demonstration scale (TRL 6–7), offering lower reagent and energy use while enabling power-to-methane integration [33]. Glycerol valorization platforms span commercial fermentation applications to pilot ‘biocircular’ schemes combining wastewater and residue streams (TRL 6–8) [85]. Thermochemical routes to advanced biofuels continue to improve through heat-integration and gas-cleanup advances [56,57,61,62]. Future directions focus on coupling DTs, process simulation, and LCA/MCDA optimization to validate performance, harmonize data across scales, and deliver circular, low-impact bioenergy chains [5,18,19].

5.3. Industrial Wastewater and Sludge Treatment

Industrial wastewater and sewage sludge are increasingly viewed as secondary resources rather than liabilities. Beyond pollutant abatement, modern plants recover phosphorus (P), nitrogen (N), and even rare metals, while advanced reuse and zero-liquid discharge (ZLD) schemes minimize freshwater withdrawals and effluent losses; key levers for circularity targets under the Green Deal and water scarcity policies [8,16,20]. Recent reviews emphasize that moving from ‘treat and dispose’ to ‘recover and recirculate’ hinges on selective separations (struvite/brushite precipitation, ammonia stripping/absorption, ion exchange), targeted sorbents and membranes for critical/precious metals, and systems integration that couples water reuse with energy and materials recovery [41,61,62,80]. LCA case studies of industrial/urban reuse further show that impacts are dominated by electricity, chemicals, and concentrate management, underscoring the value of process simulation-based inventories and realistic substitution credits for recovered products [40,90,91].

Technology landscape and target recoveries

• Nutrients (P, N). Sidestream P recovery as struvite (MgNH₄PO₄·6H₂O) or brushite is now mainstream in digestate/centrate polishing; complementary N capture relies on ammonia stripping with acid absorption or selective membranes/ion exchange, with performance contingent on pH/temperature control and scaling management [39,40,63]. Circular sludge strategies link these units to biogas lines and fertilizer markets, but LCA benefits hinge on field emission assumptions and credible mineral-fertilizer displacement [40,63].
• Rare/precious metals. Industrial effluents (electroplating, microelectronics, catalysts) enable value recovery via biosorption/ion-exchange beads, solvomet/ionic liquid extractions, and electro-winning. For example, alginate-based sorbents selectively capture rare-earth ions from low-ppm waters, supporting a ‘wastewater-as-ore’ concept, while ionic liquids have been demonstrated for Pt-group separations and as electrolytes for electrowinning [60,92,93].
• Advanced denitrification/metal co-recovery. Emerging autotrophic denitrification processes are increasingly designed for simultaneous nitrogen removal and resource recovery, reducing chemical demand and sludge generation compared to heterotrophic systems [53,94]. Among these, Fe(II)-mediated autotrophic denitrification has demonstrated the ability to couple nitrate reduction with iron bioprecipitation, producing Fe(III) phases that can co-capture phosphate and trace metals [88]. Packed-bed trials confirmed stable operation under high nitrate loads and identified optimal Fe(II)/NO₃⁻ ratios for preventing passivation, underscoring its potential for metal- and nutrient-rich industrial effluents [95]. Other redox-driven approaches include sulfur- and hydrogen-based autotrophic denitrification, which achieve complete nitrate removal without organic carbon addition, and simultaneous nitrification-denitrification-phosphorus removal (SNDPR) configurations that integrate P recovery via biological or crystallization routes [96]. Together, these bioprocesses illustrate a shift toward multi-element recovery and energy-efficient nitrogen control, consistent with circular industrial wastewater frameworks.
• Water reuse and ZLD. High-recovery trains membrane bioreactor(MBR)/reverse osmosis (RO)/nanofiltration (NF) + brine concentration via thermal or emerging electro-membrane steps) can achieve near-closed loops; however, ZLD energy use and concentrate valorization are the dominant trade-offs, with rapid innovation around forward osmosis (FO)- or electro-enabled concentration and crystallization [80,93].

Phosphorus, nitrogen, and rare metal recovery: key options and integration

• P recovery: Sidestream struvite from anaerobic dewatering liquors reduces scaling and returns marketable fertilizer; benefits improve with magnesium dosing control, CO₂-stripping integration, and digestate alkalinity management [97].
• N recovery: Ammonia stripping/acid capture (NH₄HSO₄/NH₄NO₃) and membrane contactors provide concentrated fertilizers; pairing with P recovery and heat integration lowers energy per kg-N [98].
• Rare/precious metals: Biosorbents (algal/microbial/biopolymers), tailored ion-exchange resins, and ionic liquid (IL)-based separations target Pd, Pt, rare earth elements (REEs), and In at trace levels; coupling sorption with electrowinning or calcination closes the loop to a metal product [92,99].
• Coupled N removal/Fe recovery: Fe(II)-autotrophic routes provide nitrate removal without organic carbon and enable iron capture as co-product; this is attractive where iron-rich sidestreams exist [94].
• Zero-liquid discharge approaches

Modern ZLD/minimal liquid discharge (MLD) trains combine high-recovery RO/membranes with thermal finishing (mechanical vapour compression (MVC), multiple-effect distillation (MED) and crystallizers; recent reviews highlight rapid cost/energy improvements and the need to valorize salts/solids to avoid impact shifting. Successful industrial deployments pair concentrate management (salt recovery, gypsum/NaCl) with upstream oxidation and anti-scaling control to protect membranes and evaporators [74,87]. Where full ZLD is disproportionate, ‘reuse + brine minimization’ often yields better LCA performance than absolute liquid elimination, especially when reclaimed water offsets high-impact freshwater [40,90,91].

Environmental and LCA results

• Water reuse vs. conventional discharge. LCA of industrial/urban reuse shows impacts dominated by electricity for advanced treatment and chemicals; nonetheless, reuse commonly outperforms discharge when potable water or industrial water substitution is credited and energy mixes decarbonize [90,100].
• Nutrient recovery. Fertilize substitution credits can turn sidestream P/N recovery into net climate/abiotic resource benefits; results remain sensitive to field emissions (NH₃, N₂O) and to the realistic nutrient-use efficiency of recovered products [97,98].
• ZLD/MLD. Impact hotspots are steam/electricity for brine concentration and crystallization; integrating FO/electro-deionization, heat recovery, and co-product salt markets reduces GHG and cost intensity [93].
• Rare metal recovery. Sorbent/IL routes avoid mining burdens but must control solvent footprints and end of life; case studies report promising abiotic depletion and toxicity scores when regeneration is efficient and metals displace virgin materials [92,99].

Design and optimization insights

• Integrate sidestream control with recovery: Route high-strength liquors to P/N recovery to minimize chemical use and scaling; co-design digesters, dewatering, and recovery units [97,98].
• Leverage redox-coupled bioprocesses: Autotrophic pathways using inorganic electron donors enable carbon-free nitrogen removal and can support concurrent metal or nutrient recovery [94,96].
• Prioritize realistic substitution and dynamic inventories: Use process simulation-based LCA/DT approaches to align lab pilot plant data and avoid optimistic credits for reclaimed water and fertilizers [18,19,37,90].
• Close the water-energy loop: Energy recovery in water networks (PATs/turbines) and plant heat integration can offset ZLD/advanced-reuse energy penalties, improving net impacts and OPEX [64,65,93].

Embedding these measures in DT- and AI-supported control systems enables wastewater utilities to dynamically balance resource recovery, energy use, and environmental impact.

Industrial maturity and sectoral links. P-recovery (struvite) and ammonia capture are commercially established in large WWTPs and some industrial sites (TRL 8–9), while rare metal recovery from effluents is at pilot-demo level depending on metal and matrix (TRL 5–7) [60,63,92,99]. ZLD/MLD is proven in high-salinity industries (power, chemicals) but remains energy- and capex-intensive unless co-product salts and heat integration are secured (TRL 7–9) [80,93]. Advanced bioprocesses combining nutrient recovery and metal extraction are currently being validated at pilot scale (TRL ~5–6), with several systems demonstrating stable operation under real wastewater conditions [96]. Finally, sectoral LCAs and water footprint reviews argue for pairing reuse/recovery with demand-side measures and supplier engagement to maximize absolute water and impact reductions [30,101].

5.4. Agri-Food and Biomass Processing

Agri-food and biomass residues are increasingly considered as secondary bioresources rather than waste streams, providing renewable feedstocks for lignin, protein, and fibre recovery. These sectors form the backbone of the circular bioeconomy, linking agricultural production, food industries, and material reuse in alignment with the EU Green Deal and Bioeconomy Strategy [1,20,28,41]. Recent reviews and case studies confirm that advanced valorisation technologies, combining biochemical, thermochemical, and membrane operations, can upcycle agro-industrial by-products into functional materials and biochemicals while closing energy and nutrient loops [39,50,90].

Technology landscape and target recoveries

• Lignin and cellulose fibres. Lignocellulosic residues such as cereal straw, fruit peels, husks, and bagasse are abundant low-cost feedstocks for the recovery of cellulose and lignin fractions. Mechanical, chemical, and enzymatic pulping allows production of dissolving-grade pulp for regenerated fibres, achieving purity levels suitable for textile and composite applications [102]. Plakantonaki et al. [3] demonstrated that agro-waste pulps (e.g., peach, tomato stems) can be used for viscose-type fibre spinning, provided closed-loop solvent recovery and quality control are maintained (TRL 5–6) [3,102].

Parallel delignification and hydrothermal processes yield lignin suitable for adhesives, carbon fibres, and aromatic biochemicals, supporting fossil substitution in polymer and resin industries [103].

• Protein recovery and bioplastic precursors. Protein-rich co-products from oilseed cakes, whey, feathers, or bloodmeal can be extracted and transformed into biodegradable plastics and coatings through extrusion, compression, and 3D printing [104]. Álvarez-Castillo et al. [104] reported protein biopolymers as competitive bio-based materials with mechanical properties tunable via blending and crosslinking. Life cycle indicators show up to 50–70% lower GHG intensity versus petroleum plastics, depending on renewable energy integration [37,54].
• Glucans and hemicellulosic sugars. Hydrolytic and microbial conversion of lignocellulosic sidestreams enables production of β-glucans and other polysaccharides for nutraceutical and pharmaceutical markets [105]. Abdeshahlan et al. [105] confirmed glucan recovery via enzymatic hydrolysis of agri-residues under mild pH/temperature, although economic viability remains TRL 4–5.
• Thermochemical valorization. Advanced thermochemical routes (pyrolysis, gasification, hydrothermal liquefaction) convert agri-food residues into bio-oils, syngas, and carbon materials, contributing to energy integration within agri-industrial clusters [56,60]. Berenguer et al. [106] detailed thermochemical valorisation in Portugal, showing that co-processing olive mill and winery wastes with lignocellulosic biomass enhances carbon efficiency and circularity.
• Membrane-based separations. Membrane systems (ultrafiltration (UF)/NF/RO) facilitate gentle recovery of proteins, polyphenols, and antioxidants from agri-food effluents. Integrated membrane trains, combining filtration and pervaporation, achieve high selectivity with reduced chemical demand and can be powered by renewable electricity [107].
• Bio-based construction materials. Fibre-based panels from agri-industrial and textile residues have proven viable as thermal and acoustic insulation materials, demonstrating market readiness and real circular use of regional biofibres [108].

Circular bioeconomy potential. Circular-bioeconomy strategies emphasize cascading biomass use—food → feed → materials → energy, and territorial integration of value chains [4,15,28]. As highlighted by Klein et al. [109], successful agri-food valorisation depends not only on technological feasibility but also on institutional and spatial conditions, including supply logistics, farmer–industry cooperation, and local market networks. Cross-sectoral integration between food, energy, and materials streams creates multi-value loops but also requires governance and certification frameworks to ensure environmental integrity.

At system level, integrated biorefineries that couple fermentation, AD, and thermochemical steps can exploit all biomass fractions, achieving multi-product recovery (fibres, lignin, proteins, glucans, biofuels) with minimal waste [41,51,54]. Current demonstration plants operate at TRL 5–6, validating the simultaneous production of materials, fuels, and fertilizers from real agro-industrial residues [106,110].

Environmental and LCA results. Recent LCAs indicate that environmental impacts of biomass valorization are dominated by electricity and chemicals for extraction and drying stages, while substitution credits for displaced plastics, fertilizers, or fossil energy often result in net GHG and abiotic resource benefits [37,54]. Dynamic LCA approaches integrating process simulation and DTs improve inventory accuracy and avoid optimistic credits [18,19].

Membrane recovery systems show lower impact intensity than thermal extractions, while thermochemical valorization exhibits higher energy demand but larger carbon credits through fuel displacement [60,61,106]. Regionalized assessments reveal that local sourcing and short transport distances can reduce total impacts by 20–40% [109].

Design and optimization insights.

• Integrate cascading use: Prioritize material recovery (fibres, proteins) before energy conversion, maximizing resource retention [28,41].
• Adopt mild separations: Membrane and enzymatic extractions preserve bioactivity and lower process emissions [107].
• Use process-based LCA and substitution realism: Include by-product quality and nutrient-use efficiency to avoid inflated circularity claims [37,54].
• Leverage regional synergies: Co-location of food processors, biorefineries, and construction industries supports industrial symbiosis and logistical efficiency [104,105].

When combined with digital process models and integrated LCA-TEA-MCDA analyses, these strategies form a prescriptive roadmap for optimizing cascading biomass valorisation at industrial and regional scales.

Industrial maturity and sectoral links. Most lignin and cellulose fibre recovery systems are at pilot scale (TRL 5–6) with validated continuous operation [102,103]; thermochemical valorization and integrated biorefineries reach TRL 6–7 depending on feedstock complexity [56,106]. Protein-based bioplastics have progressed to demonstration level (TRL 6–8) with active commercialization in packaging sectors [104]. Membrane recovery and natural fibre insulation materials are near commercial readiness (TRL 8–9) [107,108].

Advanced bio-based cascading systems are thus transitioning from laboratory to industrial deployment, contributing to resource efficiency, climate neutrality, and rural circularity within the broader European bioeconomy [20,28,41,54,110].

6. Integration with LCA and Optimization Strategies

Industrial circularity increasingly depends on the integration of LCA with process modelling, DTs, and optimization frameworks to enable real-time environmental performance management. Traditional LCAs, typically retrospective, are shifting toward dynamic and simulation-coupled approaches that inform both design and operational decisions [16,37,39]. This integration bridges environmental metrics with process efficiency, allowing circular systems to evolve through data-driven optimization and digital intelligence [41,42]. Figure 5 illustrates the conceptual workflow linking process simulation, dynamic LCA, and DT feedback loops, while

Table 3. Representative LCA–digital integration frameworks and TRLs.

Integration Domain	Core Method/Tool	Application Focus	Reported Benefits	TRL Range	Key References
Process Simulation LCA	Aspen Plus®, gPROMS®, integrated inventory modules	Process design, energy recovery, solvent recirculation	20–40% GHG reduction via heat integration	8–9	[40,61,83,111]
MCDA-LCA Coupling	Analytic hierarchy process (AHP), fuzzy-technique for order of preference by similarity to ideal solution (TOPSIS)	Technology ranking, circular trade-off analysis	Transparent multi-criteria prioritization	7–8	[41,112]
Digital Twin Integration	Sustainable DT Maturity Path (SDT-MP)	Water reuse, manufacturing, energy systems	Up to 30% energy savings, real-time key performance indicator (KPI) feedback	6–8	[42,113]
Artificial intelligence (AI)/machine learning (ML)-LCA Fusion	ML regression, surrogate models, neural networks	Impact prediction, dynamic inventory estimation	Automated “live” LCA updates, reduced modelling time	5–7	[25,114]

compares representative integration frameworks, tools, and TRLs.

The integration of process simulation, DTs, and AI-based surrogate models with LCA, TEA, and MCDA establishes a unified analytical framework that moves beyond retrospective assessment toward predictive and prescriptive life cycle optimization. Within this digitalized environment, real-time or scenario-based data streams can dynamically update environmental inventories, allowing the identification of concrete improvement levers such as optimal heat integration strategies, solvent and acid recirculation ratios, gas slip minimization thresholds, membrane recovery settings, and hybrid process configurations. This approach provides a cross-sector methodology capable of guiding operational and design decisions in batteries, bioenergy, wastewater, and agri-food systems, thereby positioning the digital optimization framework as a central contribution to advancing circular industrial processes.

6.1. Process Modelling and Dynamic Life Cycle Integration

At lower technology readiness levels (TRL 4–6), DTs operate primarily as analytical environments rather than real-time supervisory tools. In this context, DTs are used to couple mechanistic or data-driven process simulations with dynamic LCA, enabling early-stage scenario testing and time-resolved environmental inventory generation. These analytical DT setups support exploration of alternative design configurations, evaluation of reagent- and energy-demand profiles under varying process conditions, and integration of surrogate AI models to anticipate the effects of operational adjustments before pilot-scale validation. Such simulation-based DT-LCA coupling has been demonstrated for emerging hydrometallurgical battery recycling systems, nutrient recovery bioreactors, and bench-scale thermochemical conversion units, providing reproducible insights into environmental hotspots and optimization opportunities even in the absence of full-scale sensor networks.

Process simulation-based LCA establishes quantitative links between operating parameters and life cycle inventories. By embedding mass- and energy-balance models into environmental calculations, designers can evaluate how temperature, residence time, or recycle ratios affect emissions and resource use. Case studies in biogas upgrading, glycerol valorization, and thermochemical biomass conversion demonstrate that coupling simulation with inventory models can reduce GHG emissions by 20–40% through heat recovery and solvent recirculation [40,61,83]. In resource-intensive sectors such as battery recycling, digital process models identify electricity and reagent hotspots, improving both recovery yields and energy efficiency [69].

Recent work in circular industrial optimization highlights that dynamic inventories, continuously fed by plant data, enable ‘live LCAs’ in which each operational change recalculates environmental impacts in near real time [111]. Popowicz et al. [111] detailed how IoT, cloud databases, and DTs automate these data flows, creating continuous feedback between operation and assessment. Hybrid simulation LCA tools now combine first-principle thermodynamics with empirical data, advancing beyond static gate-to-gate analysis toward digital process twins linked to plant control systems [16,37,42,111].

Although full DT control is typically demonstrated at higher TRLs, simulation-coupled DT architectures are already used analytically in TRL 4–6 systems to support design and scenario evaluation. Examples include simulation-based LCA for emerging hydrometallurgical Li-ion battery recovery routes [69] and dynamic inventory modelling in early-stage bioreactors such as packed-bed denitrification units [95], where real-time or time-series datasets are used to update inventories and optimize operating windows.

6.2. Multi-Criteria Decision Analysis (MCDA) and Circular Trade-Offs

Because circular systems must balance environmental, economic, and social dimensions, MCDA complements LCA and techno-economic analysis (TEA). Reviews stress indicator transparency and stakeholder weighting, promoting LCA-driven MCDA as a standard for comparing valorization routes and recovery technologies [40].

Applications include ranking hydrometallurgical versus bio-hydrometallurgical metal recovery processes and comparing membrane versus thermal systems in wastewater reuse. Tools such as fuzzy-TOPSIS and the AHP quantify trade-offs among cost, climate, toxicity, and water-use criteria [34,105]. Integration with DT telemetry further allows continuous re-ranking of alternatives as feed composition, grid carbon intensity, or market prices change—transforming static assessments into adaptive decision frameworks [41,112].

6.3. Digital Twins, Artificial Intelligence, and Machine Learning

Several concrete applications illustrate how digital twins are being incorporated into the optimization of circular recovery processes. In hydrometallurgical battery recycling, DT environments integrate reactor-level leaching kinetics, mass-transfer models, and separation-unit simulations with dynamic LCA modules, enabling continuous assessment of reagent consumption, electricity demand, and emission profiles under variable operating conditions. In wastewater treatment, DT-supported nutrient recovery trains link online monitoring data, including dissolved oxygen, ammonium, nitrate, conductivity, and pH, to dynamic LCA models to capture temporal fluctuations in energy use, nitrous oxide emissions, and nutrient recovery efficiencies. For thermochemical systems, AI-enhanced DT architectures have been applied to pyrolysis and hydrothermal liquefaction, using predictive optimization algorithms to identify optimal temperature windows, residence times, and catalyst dosages based on environmental objective functions. These applications demonstrate how DT-LCA integration supports both operational optimization and design-stage environmental assessment across multiple recovery pathways.

DTs are virtual representations of assets or systems that couple real-time sensing, physics-based modelling, and AI analytics. They bridge process control with sustainability performance, converting LCA metrics into operational variables [42]. Sajadieh and Noh [42] outlined a Sustainable Digital Twin Maturity Path (SDT-MP) that progresses from descriptive monitoring to AI-enabled prescriptive control in circular manufacturing.

Industrial implementations show that DT-enabled systems can achieve up to 30% energy savings and substantial material-loop closure, particularly in energy networks, water reuse plants, and remanufacturing lines [42,113]. Within these digital environments, environmental KPIs (e.g., GHG emissions, water use, waste) become live dashboard variables directly linked to control strategies.

Concurrently, AI, IoT, big data analytics, and blockchain are being embedded across the ISO 14040 framework:

• Blockchain ensures data traceability and material accountability;
• IoT provides high-frequency measurement for inventory updates;
• AI/ML supply surrogate models for process prediction;
• Big data pipelines enable forecasting and uncertainty propagation [39,42,111].

Machine learning-enhanced LCAs demonstrate improved prediction of emission factors, product quality, and process variability, enabling impact-aware optimization [25,115]. Romeiko et al. [25] reported that ML can reduce LCA modelling time by more than half while maintaining predictive accuracy, and Salla et al. [115] showed that surrogate models embedded in process simulators can maintain near-real-time impact calculation.

Validation and traceability of AI-based surrogate models are essential for their integration into LCA workflows. Reviewed studies employ train-test splits, k-fold cross-validation, and out-of-sample error metrics (e.g., mean absolute error (MAE), root mean square error (RMSE) to compare surrogate predictions against full process simulation LCAs, with reported deviations typically within 5–15% when high-quality inventory data are used. Model transparency is increasingly supported through interpretability frameworks such as SHAP and LIME, while reproducible, version-controlled simulation LCA pipelines and, in some cases, blockchain-based provenance layers are used to document data sources and modelling assumptions. These measures collectively strengthen confidence in AI-assisted environmental predictions and ensure that surrogate models remain verifiable and traceable.

Such implementations demonstrate that DT-like analytical integration is already reproducible at TRL 4–6 when coupled with process simulation and dynamic LCA. For example, recent DT-enabled wastewater reuse studies [18,19] use calibrated models to iteratively update environmental inventories, energy demands, and by-product flows under varying influent conditions, providing early-stage optimization without requiring full operational DT maturity.

When coupled with LCA, TEA, and MCDA, DTs and AI models evolve from diagnostic tools into prescriptive decision support systems. They provide operational recommendations such as optimal reactor conditions, selective reagent dosing, nutrient or metal recovery strategies, and energy integration settings under dynamic feed, load, or market conditions. This capability forms the analytical foundation for the sector-specific optimization insights presented in the case studies, underscoring that the review’s primary contribution lies in demonstrating how digital optimization and life cycle analytics jointly inform real-time, impact-aware industrial decision making.

6.4. Environmental Optimization and Insights

Building on the integrated DT and AI framework described above, the following insights summarize the prescriptive environmental optimization levers consistently identified across sectors and recovery technologies.

Across industrial sectors, LCAs consistently identify electricity use, chemical reagents, and waste gas control as dominant impact hotspots. Heat recovery, solvent and membrane recycling, and emission slip reduction remain the most effective improvement levers [40,61,69,80,83]. Process simulation LCAs clarify when hybrid trains (e.g., pyro-hydro or membrane-bio) outperform single routes, while MCDA frameworks quantify uncertainty and trade-off risks [41,69].

Cross-sectoral synergies emerge clearly from the reviewed evidence. Heat integration strategies in bioenergy parallel solvent recovery and acid recirculation levers in hydrometallurgy, while nutrient-loop closure in wastewater systems aligns directly with digestate and biochar valorization pathways in bioenergy. Membrane technologies used for polyphenol or protein recovery in agri-food systems show strong transferability to industrial water reuse and brine minimization. DT and AI-assisted optimization frameworks also reveal structural convergence: electricity hotspots, reagent intensity, CH₄/CO₂ slip, and effluent treatment consistently dominate environmental burdens across sectors, enabling similar prescriptive levers such as heat recovery, solvent/membrane recirculation, hybrid pyro-hydro or bio-chemical configurations, and dynamic set-point adjustments informed by live LCA indicators. TRL analysis further shows meaningful complementarities between mature (TRL 7–9) systems, such as hydrometallurgy, AD, and membrane purification, and emerging biotechnological or thermochemical routes (TRL 4–6), where DT-enabled simulation and dynamic LCA can support early-stage optimization. Together, these synergies demonstrate that circular optimization levers are structurally similar across otherwise heterogeneous sectors, and that EF 3.x-aligned modelling practices enable cross-sector methodological coherence.

DTs and AI-based controllers now operationalize these insights, feeding LCA results directly into supervisory control systems to maintain low-impact operation [42,112]. Industrial demonstrations confirm that connecting LCA, process simulation, and optimization layers reduces both energy demand and life cycle impacts, while improving resilience and uptime [25,111].

6.5. Industrial Maturity and Implementation Pathways

Process simulation LCAs are commercially proven (TRL 8–9) for process design and retrofit evaluation. MCDA-LCA frameworks have reached TRL 7–8 for technology selection and policy planning. DT integration with environmental KPIs is rapidly scaling from pilot to demonstration (TRL 6–8) across manufacturing, water treatment, and energy network systems [42,111,112,113].

AI-assisted LCAs and surrogate modelling currently operate at TRL 5–7, with validation ongoing through industrial testbeds and public–private innovation platforms [113,114,115].

For emerging biotechnological and chemical processes at TRL 4–6, the role of DTs remains analytical rather than operational, supporting simulation-based impact prediction, hotspot control, and design stage optimization prior to full-scale deployment.

Future circular manufacturing will depend on impact-aware control systems, where LCA indicators dynamically steer production within sustainability limits. By coupling process models, real-time data, and optimization algorithms, industry can close the gap between assessment and action, realizing proactive, data-driven circularity consistent with the European Green Deal and CRMA [20,21,42,115]. Integrating EF-consistent modelling (PV/CFB-IND) with sector-scale circularity evidence would make future by-product optimization studies directly usable in EU implementation [44,45,46,47].

A comparison of current TRLs with EU 2025–2030 targets indicates that only a subset of circular process technologies are sufficiently mature to support near-term implementation. Hydrometallurgical battery recycling, biomethane upgrading, and high-recovery water reuse systems already operate at TRL 7–9 and can realistically contribute to EU Green Deal and CRMA timelines. In contrast, biotechnological metal recovery routes, CO₂-based biomethanation, and advanced thermochemical fuel pathways remain at TRL 4–6, where deployment depends on accelerated demonstration funding, scale-up infrastructures, and regulatory flexibility. This misalignment highlights that policy ambition exceeds industrial readiness in several emerging domains, underscoring the need for coordinated innovation support.

7. Policy, Economic, and Environmental Considerations

7.1. Policy Frameworks Enabling Circular Industry

European initiatives, such as the Green Deal [20] and the CRMA [21], provide the legislative backbone for industrial circularity. Both emphasize resource efficiency, digital traceability, and material recovery, mandating minimum recycling rates and the creation of Strategic Projects for secondary raw materials. These frameworks directly support the technologies reviewed in Section 3, Section 4 and Section 5, from nutrient and metal recovery to agri-food valorization.

Analyses by Goergescu et al. [116] and Baldassare et al. [117] show that coherent implementation depends on standardized assessment tools, particularly LCA, TEA, and MCDA, integrated with digital data infrastructures. Harmonized environmental reporting and interoperability across EU data spaces are now recognized as prerequisites for scaling industrial symbiosis and DT-based sustainability control [25,35,111,112,113].

Quantitatively, the effectiveness of these policies is reflected in mandated performance thresholds and measurable deployment trends. Regulation (EU) 2023/1542 requires 50–80% lithium and 90–95% cobalt, nickel, and copper recovery in industrial battery systems by 2030, thresholds already achieved by several commercial hydrometallurgical and hybrid pyro-hydro facilities in the EU. Likewise, the REPowerEU biomethane target of 35 bcm by 2030 has accelerated production to approximately 18–20 bcm in 2023, supported by more than 1000 operational biomethane plants. These indicators demonstrate that policy-driven circularity obligations are leading to quantifiable improvements in material recovery and renewable gas deployment.

7.2. Techno-Economic and Environmental Assessment

Coupling TEA with life cycle modelling clarifies trade-offs between cost, energy use, and environmental impact across valorization pathways.

• In battery recycling, hybrid pyro-hydrometallurgical systems achieve 30–40% lower levelized costs than stand-alone routes by reusing process heat and reagents [68,73].
• Bio-hydrometallurgical recovery remains slower but achieves favourable economics under low-energy and reagent recirculation scenarios [71,76].
• Nutrient recovery integrated with AD yields internal rate of returns (IRRs) of 8–15%, especially when digestate valorization offsets fertilizer imports [40,63,85,97].
• Agri-food cascades combining fibre, protein, and energy recovery can match fossil-based products at regional scales (<100 km feedstock radius) [102,103,104,105,106].

Dynamic TEA-LCA coupling links process simulation data with cost and emission models in real time [112]. This integration enables decision-makers to evaluate both profitability and impact during design and operation [18,19,113].

Several EU-funded demonstration facilities illustrate this policy–technology linkage. Large-scale hydrometallurgical recycling plants (e.g., Umicore, Northvolt) already meet the recovery and carbon footprint requirements of the CFB-IND rulebook, while advanced nutrient recovery and membrane-based agri-food cascades deployed under Horizon Europe and Innovation Fund projects demonstrate measurable cost savings and emission reductions at TRL 7–9.

7.3. Environmental and Socio-Economic Performance

Integrated recovery systems deliver substantial environmental gains:

• GHG reductions of 20–50% through heat integration and renewable inputs [40,61,83];
• Resource depletion mitigation via closed-loop Co/Ni/Li recovery [70];
• Nutrient loss prevention through struvite or ammonium-salt fertilizers [97,98];
• Water footprint reductions > 90% in advanced reuse and ZLD configurations [93,100,101].

Socio-economically, such infrastructures create localized employment, enhance supply resilience, and align with the Just Transition Mechanism by revitalizing post-industrial regions [20,21].

7.4. Barriers, Integration Pathways, and Policy Alignment

Despite growing policy momentum, several barriers continue to constrain circular-bioeconomy deployment and the integration of biotechnological and industrial systems.

Economic barriers include high capital expenditure (CAPEX), uncertain returns on secondary raw materials, and volatility in carbon markets. Mitigation strategies involve the introduction of material credits, carbon pricing mechanisms, and preferential financing instruments that reward low-emission technologies [21,42].

Regulatory barriers arise from fragmented end-of-waste criteria and inconsistent permitting frameworks across EU Member States. Harmonization through the Green Deal Data Space and the CRMA seeks to streamline cross-border exchange and traceability of recovered materials [20].

Technological barriers remain particularly acute in the integration of biological, electrochemical, and separation processes. DT modelling, AI-based control, and smart monitoring systems are increasingly enabling process stability, scalability, and predictive optimization [25,42,110,111,112,113,114,115].

Social and governance barriers reflect the need for transparency and stakeholder participation in emerging industrial symbiosis zones and regional innovation ecosystems [15,28].

Regional policy divergence further shapes deployment trajectories. EU regulatory frameworks emphasize harmonized footprint rules (EF 3.x), mandatory recovery targets, and strict traceability requirements, while U.S. and Asian regulatory systems rely more heavily on market-based incentives, voluntary certification schemes, and heterogeneous end-of-waste regulations. These differences create varying levels of investment certainty, permitting speed, and technology uptake, highlighting the need for interoperable assessment standards to enable cross-regional scaling of circular process technologies.

To overcome these challenges, coordinated policy–technology–finance alignment is essential. Embedding multi-criteria evaluation frameworks, combining LCA, TEA, and MCDA, into project design and regulatory assessment provides quantitative consistency between environmental benefits and economic incentives.

At the same time, the convergence of digitalized assessment, process simulation, and policy regulation is reshaping industrial planning. Linking IoT-based monitoring with real-time LCA and TEA enables enterprises to maintain compliance while optimizing resource efficiency [18,19,42]. Standardized metrics and interoperable digital infrastructures under the Green Deal Industrial Plan are expected to validate these integrated assessment frameworks at TRL 7–9, transforming assessment into actionable performance control.

Finally, cross-sectoral knowledge transfer, particularly among energy, water, and agri-food systems, will be decisive in ensuring that circular manufacturing transitions from voluntary commitments to regulated, evidence-based implementation. Together, these developments strengthen the feedback loop between innovation and governance, ensuring that industrial transformation proceeds within verifiable environmental and economic boundaries.

8. Conclusions

By-product recovery is a key driver of circular and low-impact industrial systems, and the evidence across batteries, bioenergy, wastewater, and agri-food sectors shows that the most effective solutions rely on integrated process configurations supported by digital tools. Heat and water integration, solvent and reagent recirculation, emission-slip reduction, and realistic substitution modelling consistently emerge as improvement levers, indicating strong cross-sectoral synergies despite technological diversity. A central contribution of this review is the articulation of a unified framework linking LCA, TEA, MCDA, and digital tools such as digital twins, AI surrogate models, and process simulation. When aligned with EU EF 3.x and JRC rulebooks, this integration enables predictive and prescriptive environmental optimization and strengthens the regulatory relevance of circular process assessments. Nevertheless, challenges remain, including heterogeneous data quality, sector-specific FUs, variable TRL maturity, and the need for validated and transparent AI-assisted LCA workflows. Looking ahead, progress will depend on continued harmonization of data and methods, deeper integration across industrial sectors through resource-loop coupling and regional symbiosis, and the maturation of digital twin and AI systems capable of supporting real-time, impact-aware operational decisions. Strengthening the interface between digitalization, life cycle assessment, and policy frameworks will be essential to scale robust, verifiable, and economically resilient circular industrial systems.