Process Systems Engineering for Environmental Protection: Overview on Methods, Models, and Applications

Over the past decade, Process Systems Engineering (PSE) has undergone a significant transformation, evolving from a discipline primarily focused on process optimization and control to a key enabler of environmentally sustainable industrial development. This evolution has been driven by the urgent need to address global environmental challenges such as climate change, resource depletion, and pollution [1].

One of the most significant advancements in PSE for environmental protection lies in the shift from isolated process optimization to holistic process design and optimization. This paradigm emphasizes the integration of environmental objectives alongside traditional economic and technical criteria, enabling the development of processes that are not only efficient and cost-effective but also environmentally benign. At its core, holistic process design involves the simultaneous consideration of multiple interrelated factors—including energy and material efficiency, emissions reduction, waste minimization, and resource circularity—across the entire process life cycle. This approach contrasts with conventional sequential design methodologies, where environmental considerations are often addressed post hoc, leading to suboptimal or reactive solutions.

Hrithik P. M. et al. [1] present a time-series forecasting approach for CO₂ emissions in India using the ARIMA model, addressing the critical need for accurate environmental predictions to support climate policy. The study applies the Box–Jenkins methodology, incorporating stationarity checks, ACF/PACF analysis, and model selection via AIC to ensure robustness. Results demonstrate that ARIMA (0,2,4) effectively captures historical emission trends and provides reliable short- to medium-term forecasts, validated through error metrics such as RMSE and MAPE. By enabling evidence-based decision-making, this work exemplifies how data-driven predictive modeling within Process Systems Engineering can guide strategic interventions for carbon mitigation and sustainable development planning.

Recent methodological advances have enabled the incorporation of multi-objective optimization techniques that balance trade-offs between conflicting goals, such as minimizing operational costs while reducing carbon footprint or water usage. These techniques often employ mixed-integer nonlinear programming (MINLP), evolutionary algorithms, or multi-criteria decision analysis (MCDA) to navigate complex design spaces and identify Pareto-optimal solutions. Moreover, the integration of process simulation tools (e.g., Aspen Plus, gPROMS) with environmental assessment frameworks (e.g., Life Cycle Assessment, exergy analysis) has facilitated the evaluation of environmental impacts during the early stages of process development [2]. This integration allows for the identification of environmental hotspots and the design of mitigation strategies before capital-intensive decisions are made. Another key aspect of holistic optimization is the consideration of process integration opportunities, such as heat and mass integration, which can significantly enhance energy efficiency and reduce utility consumption. Techniques like pinch analysis, heat exchanger network synthesis, and water network optimization are increasingly being embedded within broader PSE frameworks to ensure systemic improvements.

Furthermore, the emergence of data-driven modeling and machine learning has opened new avenues for real-time optimization and adaptive process control, enabling systems to respond dynamically to changing environmental conditions, feedstock variability, and operational disturbances. These capabilities are particularly relevant in the context of integrating renewable energy sources and managing decentralized production systems.

Recent research on mechanoactivation-assisted nitric–sulfuric leaching of molybdenite concentrates demonstrates how Process Systems Engineering methodologies can significantly enhance the sustainability of metallurgical operations [3]. The study optimizes acid concentration, temperature, and leaching time using response surface methodology, achieving up to 72.6% molybdenum recovery under combined nitric–sulfuric acid conditions with oxygen sparging. By integrating experimental design, mechanochemical activation, and data-driven optimization, the approach reduces environmental burdens associated with traditional pyrometallurgical routes, such as CO₂ and SO₂ emissions, while improving process efficiency. This example highlights the role of multi-objective optimization and hybrid experimental–computational workflows in developing cleaner, more efficient extraction technologies aligned with circular economy principles.

A key contribution in this Special Issue is the work of Pastor et al. [4], which proposes the re-ISSUES model (Renewable Energy-Linked Interoperable Smart and Sustainable Urban Environmental Systems). This model integrates urban environmental management with renewable energy systems using semantic and technical interoperability approaches. It is based on citizen science, the use of low-cost IoT sensors, and collaboration with local renewable energy companies, all within a systems engineering methodology. The study presents a motivational case study focused on urban odor management, quantifying the costs and benefits of implementing environmental measurement and verification systems (EMVSs) and exploring opportunities to optimize their economic sustainability through digitalization, circular economy, and citizen participation strategies. Furthermore, the re-ISSUES model is validated using ontologies and an analysis of scientific literature, demonstrating its potential to improve urban air quality and foster energy resilience in smart cities.

Wentian Lu et al. [5] present an analytical framework for optimizing energy storage participation in primary frequency regulation within low-carbon power systems. The study introduces a reduced second-order aggregation model and compensation-based design to calculate virtual inertia and damping coefficients for distributed energy resources (DERs), ensuring grid stability under high renewable penetration. Additionally, an adaptive control strategy dynamically adjusts these parameters based on the state of charge (SOC), enhancing resource utilization and preventing operational constraints. By combining model simplification with SOC-aware optimization, the approach supports responsive and sustainable frequency control, exemplifying how Process Systems Engineering contributes to decarbonized and resilient energy infrastructures.

Guayanlema et al. [6] analyze the long-term benefits of promoting electric cooking in Ecuador as a strategy to replace liquefied petroleum gas (LPG) in the residential sector. Using the LEAP (Long-range Energy Alternative Planning) model, the study quantifies reductions in LPG demand, associated green-house gas emissions, and subsidy expenditures under different policy scenarios. Results indicate that widespread adoption of induction stoves, supported by targeted incentives, can significantly lower carbon emissions, enhance energy sovereignty, and contribute to national climate commitments. This work exemplifies how Process Systems Engineering integrates long-term energy planning with environmental objectives, aligning household-level interventions with system-wide sustainability goals.

In the context of PSE for environmental protection, Life Cycle Thinking (LCT) and environmental assessment (EA) have emerged as indispensable tools for guiding sustainable process development. While traditional process design often focuses on optimizing operational performance within plant boundaries, LCT expands the scope to encompass the entire life cycle of a product or process, from raw material extraction through production, use, and end-of-life disposal or recycling [7].

The integration of Life Cycle Assessment (LCA) into PSE frameworks enables a quantitative evaluation of environmental impacts across multiple categories, such as global warming potential, eutrophication, acidification, and resource depletion. This comprehensive perspective is essential for identifying environmental trade-offs that may not be apparent when focusing solely on direct emissions or energy use. For instance, a process modification that reduces energy consumption may inadvertently increase water usage or generate more hazardous waste—impacts that LCA can help uncover and mitigate [8,9,10].

From a methodological standpoint, recent advances have facilitated the coupling of LCA with process simulation and optimization tools, allowing for dynamic and iterative assessments during the design phase. This coupling supports eco-design strategies, where environmental performance indicators are treated as design objectives or constraints within multi-objective optimization problems. Techniques such as goal programming, ε-constraint methods, and evolutionary multi-objective algorithms are increasingly employed to navigate the trade-offs between economic and environmental criteria [11].

Moreover, LCT complements holistic process design and optimization by ensuring that sustainability is not only achieved at the unit or plant level but also across the broader value chain. This is particularly relevant in the design of biorefineries, waste valorization systems, and renewable energy integration, where upstream and downstream impacts can significantly influence the overall sustainability profile [12].

The adoption of hybrid LCA approaches, which combine process-based and input–output models, further enhances the robustness of environmental assessments, especially in complex systems with multiple interdependencies. Additionally, the emergence of dynamic LCA allows for time-dependent impact assessments, which are crucial for evaluating systems with variable performance over their operational lifespan, such as those incorporating intermittent renewable energy sources.

The ongoing digital transformation of the process industries—often referred to as Industry 4.0—has introduced a new paradigm in PSE, enabling the realization of smart manufacturing systems that are more adaptive, efficient, and environmentally sustainable. Digitalization provides the technological foundation for integrating real-time data, advanced analytics, and intelligent control into process design and operation, thereby enhancing the capacity of PSE to address environmental challenges. At the heart of this transformation is the digital twin, a virtual replica of a physical process or system that continuously receives data from sensors and control systems. Digital twins enable real-time monitoring, simulation, and optimization, allowing engineers to predict system behavior under varying conditions, detect anomalies, and implement corrective actions proactively. This capability is particularly valuable for minimizing energy consumption, reducing emissions, and ensuring compliance with environmental regulations.

Machine learning (ML) and artificial intelligence (AI) further augment the decision-making capabilities of PSE frameworks. By learning from historical and real-time data, ML models can uncover complex, nonlinear relationships between process variables and environmental performance indicators. These insights can be used to develop data-driven surrogate models that accelerate optimization routines, or to implement predictive control strategies that dynamically adjust operations to maintain optimal environmental performance under uncertainty [13].

Digitalization also enhances the implementation of Life Cycle Thinking (LCT) by enabling the integration of real-time environmental data into Life Cycle Assessment (LCA) models. This allows for dynamic LCA, where environmental impacts are assessed as a function of time and operational conditions, providing a more accurate and responsive basis for sustainability-oriented decision-making. For example, the environmental footprint of a process can be continuously updated based on actual energy mix variations, feedstock changes, or process disturbances [14].

Furthermore, smart manufacturing systems facilitate closed-loop optimization, where feedback from environmental performance metrics (e.g., emissions, energy use, and waste generation) is used to iteratively refine process operations. This aligns with the principles of holistic process optimization, ensuring that environmental objectives are not only considered during the design phase but are actively pursued throughout the operational life cycle. The integration of cloud computing, industrial Internet of Things (IIoT), and edge computing further supports distributed data acquisition and processing, enabling decentralized decision-making in complex, multi-unit, or multi-site systems. This is particularly relevant for renewable energy integration, waste valorization networks, and smart grids, where system dynamics are influenced by external factors such as weather variability, market conditions, and regulatory constraints [15].

The transition from a linear “take-make-dispose” industrial model to a circular economy (CE) paradigm represents a fundamental shift in how resources are managed across the life cycle of products and processes [16]. Within the framework of PSE, this transition is catalyzed by the development of advanced methodologies for resource recovery, process integration, and system-wide optimization, all aimed at minimizing waste and maximizing value retention. In a circular economy, the emphasis is placed on closing material and energy loops, thereby reducing the extraction of virgin resources and the generation of waste. PSE contributes to this goal by enabling the systematic design and optimization of closed-loop systems, such as waste valorization networks, industrial symbiosis, and integrated biorefineries. These systems are designed to recover valuable materials and energy from waste streams, transforming them into secondary raw materials or energy carriers that can be reintegrated into production cycles. A key enabler of circularity in PSE is the integration of process synthesis and optimization techniques that consider multiple pathways for resource recovery. For example, superstructure-based optimization models can be used to evaluate alternative configurations for waste treatment, recycling, and reuse, identifying the most sustainable and cost-effective options. These models often incorporate multi-objective criteria, balancing environmental benefits (e.g., reduced emissions, lower resource depletion) with economic performance [17].

The synergy with Life Cycle Thinking (LCT) is particularly important in this context. By embedding Life Cycle Assessment (LCA) into the design and evaluation of circular systems, engineers can ensure that resource recovery strategies do not lead to unintended environmental burdens elsewhere in the system. For instance, the energy required for recycling or reprocessing must be weighed against the environmental savings from avoided raw material extraction. Digitalization and smart manufacturing further enhance the implementation of circular economy principles by enabling real-time monitoring and control of resource flows. Through the use of sensor networks, blockchain-based traceability, and data analytics, it becomes possible to track material usage, detect inefficiencies, and optimize recovery processes dynamically. Digital twins of circular systems can simulate the impact of different operational strategies or policy interventions, supporting decision-making under uncertainty [8,9,10].

Moreover, process integration techniques such as heat and mass exchange networks, pinch analysis, and water reuse optimization are instrumental in designing systems that minimize utility consumption and internalize waste streams. These approaches align with the broader goals of holistic process optimization, ensuring that circularity is not an afterthought but a foundational design principle.

The pursuit of environmental sustainability in industrial systems necessitates a shift from isolated process-level optimization to a broader, systems-level perspective that encompasses entire supply chains and energy systems. In this context, PSE provides a powerful set of modeling, simulation, and optimization tools to design and manage sustainable supply chains and low-carbon energy systems, aligning operational decisions with environmental and economic objectives [18].

A sustainable supply chain integrates environmental considerations into every stage of the product life cycle—from raw material extraction and manufacturing to distribution, use, and end-of-life management. PSE contributes to this integration by enabling multi-scale modeling and optimization of supply chain networks, accounting for factors such as transportation emissions, resource availability, energy consumption, and waste generation. These models often employ mixed-integer linear/nonlinear programming (MILP/MINLP) and stochastic optimization to handle the inherent complexity and uncertainty in global supply chains [19,20,21].

The incorporation of LCA into supply chain modeling further enhances sustainability by quantifying the environmental impacts of different configurations and sourcing strategies. This allows decision-makers to evaluate trade-offs between cost, carbon footprint, and resource efficiency, and to identify hotspots where interventions can yield the greatest environmental benefits. For example, sourcing raw materials from geographically closer suppliers may reduce transportation emissions, while also improving supply chain resilience.

In parallel, energy systems modeling has become a critical area within PSE, particularly in the context of the global transition toward decarbonized and decentralized energy systems. PSE methodologies support the design and operation of integrated energy systems that combine renewable energy sources, energy storage, demand-side management, and sector coupling (e.g., power-to-heat, power-to-gas). These models often rely on multi-period optimization, agent-based modeling, and scenario analysis to evaluate the performance of energy systems under varying demand profiles, policy constraints, and technological developments.

The role of digitalization is particularly pronounced in this domain. The availability of high-resolution data from smart meters, IoT devices, and geospatial information systems (GISs) enables the development of data-driven models for real-time monitoring and optimization of supply chains and energy networks. Digital twins of supply chains and energy systems can simulate the impact of disruptions (e.g., supply shortages, price volatility, and extreme weather events) and support resilient planning and adaptive control strategies [22].

Moreover, multi-agent systems and blockchain technologies are being explored to enhance transparency, traceability, and coordination across decentralized supply chains and energy markets. These tools facilitate peer-to-peer energy trading, dynamic pricing, and collaborative logistics, all of which contribute to reducing environmental impacts while maintaining economic viability.

In synergy with holistic process design, Life Cycle Thinking, and smart manufacturing, sustainable supply chain and energy systems modeling enables a comprehensive approach to environmental protection. By extending the scope of optimization beyond the plant level to encompass entire networks and infrastructures, PSE empowers stakeholders to make informed decisions that align with the principles of circular economy, climate neutrality, and resource stewardship.

This Special Issue demonstrates the pivotal role of Process Systems Engineering (PSE) in addressing complex environmental challenges through advanced modeling, optimization, and integration strategies. The contributions collectively highlight how PSE enables multi-scale decision-making, coupling process-level improvements with system-wide sustainability objectives. Key technical insights include the integration of Life Cycle Assessment (LCA) with process simulation for dynamic eco-design, the deployment of digital twins for real-time optimization, and the application of multi-objective optimization frameworks to balance economic and environmental trade-offs.

Despite these advances, several research gaps and opportunities remain. Future investigations should focus on the following:

• Development of hybrid modeling approaches combining first-principles, data-driven, and AI-based methods to enhance predictive accuracy under uncertainty.
• Formalization of interoperability standards for environmental and energy systems, enabling semantic and technical integration across heterogeneous platforms.
• Expansion of dynamic LCA methodologies to incorporate real-time operational data streams, supporting adaptive sustainability assessments.
• Design of resilient supply chain and energy network models that integrate stochastic optimization and agent-based simulation for climate risk mitigation.
• Exploration of blockchain and distributed ledger technologies for transparent carbon accounting and traceability in circular economy frameworks.
• Advancement of digital twin architectures for multi-domain systems, including coupling with uncertainty quantification and scenario-based policy analysis.

By addressing these research directions, PSE can further strengthen its role as a cornerstone of sustainable industrial transformation, bridging the gap between theoretical innovation and practical implementation in pursuit of climate neutrality and resource efficiency.