Addressing Challenges in Large-Scale Bioprocess Simulations: A Circular Economy Approach Using SuperPro Designer

Abstract

Bioprocess simulation is a powerful tool for leveraging circular economy principles in the analysis of large-scale bioprocesses, enhancing decision-making for efficient and sustainable production. By simulating different process scenarios, researchers and engineers can evaluate the techno-economic feasibility of different approaches. This approach enables the identification of cost-effective and sustainable solutions, optimizing resource use and minimizing waste, thereby enhancing the overall efficiency and viability of bioprocesses within a circular economy framework. In this review, we provide an overview of circular economy concepts and trends before discussing design methodologies and challenges in large-scale bioprocesses. The analysis highlights the application and advantages of using process simulators like SuperPro Designer v.14 in bioprocess development. Process design methodologies have evolved to use specialized software that integrates chemical and biochemical processes, physical properties, and economic and environmental considerations. By embracing circular economy principles, these methodologies evaluate projects that transform waste into valuable products, aiming to reduce pollution and resources use, thereby shifting from a linear to a circular economy. In process engineering, exciting perspectives are emerging, particularly in large-scale bioprocess simulations, which are expected to contribute to the improvement of bioprocess technology and computer applications.

1. Introduction

This review focuses on the use of specialized software for bioprocess design, such as SuperPro Designer v.14 (Intelligen, Inc., Scotch Plains, NJ, USA), to perform techno-economic analysis of various bioprocess configurations aimed at producing biofuels such as biogas, hydrogen, bioethanol, biobutanol, and biodiesel, using wastewater and lignocellulosic biomass residues, as well as liquid and solid residues from the food industry as raw materials. The production of other chemicals of industrial interest is also analyzed, e.g., for PHB, lipids, carbohydrates, organic acids, and biopharmaceutical compounds.

Process design is how large-scale production is accomplished from initial concepts and lab-scale results for producing valued molecules for societies. This is a time-consuming engineering activity; the more accurate the projections from process design are, the less resources are needed. Thus, improved methods for process and bioprocess design have been developed, such as process simulation in digital environments, to deliver accurate design results in the least possible time.

The use of SuperPro Designer is important for the optimization and simulation of bioprocesses commonly studied at lab-scale, identifying the most efficient and sustainable routes from both an economic and environmental perspectives. It also helps in identifying potential technological and economic barriers that could limit the industrial-scale implementation of these circular economy bioprocesses.

In this context, the integration of industrial residues as raw materials not only promotes sustainability but also reduces costs and minimizes final environmental impact, thus encouraging the adoption of circular economy practices in industry. The ability to perform comprehensive analysis using tools like SuperPro Designer is crucial for developing viable and sustainable production strategies in the circular economy, ensuring optimal utilization of available resources.

This contribution presents a general overview of circular economy concepts and trends before discussing design methodologies and challenges in large-scale bioprocesses. Previous works have dealt with the importance of circular economy, which are now added with difficult technical tasks when changing the process scale from the lab to the large-production level. This analysis leads to us presenting the application and advantages of using process digital simulators such as SuperPro Designer in bioprocess development. Actually, digital simulation for the design of circular economy processes is now gaining momentum because of the fast results it can provide.

2. Importance of Circular Economy on Industrial Development and Biotechnology

Several recent review articles on circular economy (CE) present an extensive survey of scenarios in which this trend of organizing the economy has been implemented to some extent. These literature reports mostly include the term CE in connection with microalgal biorefinery [1,2,3,4,5,6] and the valorization of residues from various sources [7,8,9,10,11,12,13]. The most remarkable review on CE is perhaps the one published in 2016 by Ghisellini et al. [14], who revised over two decades of reports on CE studies and implementation worldwide. According to the authors [14], “CE may be considered as a way to design an economic pattern aimed at increasing the efficiency of production by means of the appropriate use, reuse and exchange of resources doing more with less”. In their review, the scientists focused their literature survey and research on certain aspects of circular economy principles and highlighted that the European Union, Japan, the US, and China are the countries where CE is most advanced, primarily due to the challenges of waste management [14]. Support for the CE has been demonstrated through regulations such as the Circular Economy Action Plan (European Green Deal) and the United Nations Sustainable Development Goals (SDGs), specifically SDG 7 (affordable and clean energy) and SDG 12 (Sustainable consumption and production), which are directly aligned with CE [15,16]. The relatively recent ISO 59000 family of standards was designed to promote CE practices and transitions [17]. According to the literature, CE now includes metrics at various levels, from micro, meso, to macro levels. Multiple indexes have been proposed to quantitatively assess the impact of CE on industries. For example, at the micro level, which comprises products, components, and materials, there are 18 indicators to evaluate the economic, environmental, and social aspects impacted by CE implementation. These micro level indicators are included in the Material Circularity Indicator. At the meso level, for example, the industrial park CE has been used to evaluate resource output, consumption, utilization, and waste disposal. At the macro level, five key areas are analyzed: production and consumption, waste management, secondary raw materials, competitiveness and innovation, and global sustainability and resilience. These are evaluated with indicators of the Regional CE Development Index [18].

The principles of CE include optimal design, which must encompass assembly and disassembly, reuse and recycling, and the optimal product life scenario. These principles are primarily considered in research published from 2011 to 2014. Other principles include reduction, reuse, and recycling, which involve aspects such as designing durable products, maximizing the technical reusability of materials, developing take-back mechanisms from companies, reinforcing local markets for recycled materials, the feasibility of cellulose being reused 4–6 times, and food waste, which requires high costs in research and development for further transformation, with several reports published between 2013 and 2015. CE principles also include the reclassification of materials into technical nutrients (observed in the concept of biorefinery) and those suitable for obtaining renewable energy. These principles deal with reuse after the first cycle, safe return to the biosphere, or a cascade of subsequent uses (biorefinery), aiming to increase their share compared to fossil fuels.

Interestingly, at the 10th international conference on Bioprocessing held in Taiwan in 2022 [19], which focused on “Emerging Trends in Industrial Bioprocessing: Focus on Sustainability and Circular Bioeconomy,” the organizers published a document highlighting the conference. This document included the keywords of the research presented in the articles derived from the event. The term “bioprocesses” was the most frequently used keyword, followed by “valorization,” “microbial,” “fermentation,” “waste removal,” “processes,” “agro-industrial,” “challenges,” and “catalysts,” among others [19]. This demonstrates the growing global interest in merging knowledge and applications from various disciplines such as food technology and engineering, industrial and environmental biotechnology, and industrial bioprocesses to aid in the implementation of CE principles. Regarding biotechnological processes used in the biorefinery approach for CE, a considerable amount of research has focused on macroalgal-based biorefineries since lignocellulosic biomass-based processes present more challenges due to the cost of pretreatments needed before the biomass can yield valuable bioproducts (e.g., thermochemical or chemical pretreatments). Macroalgal-based bioprocesses are also preferred over microalgal ones due to the higher costs involved in harvesting and extraction processes [20]. Thus, macroalgal biorefineries are a good example of CE based on the valorization of renewable biomass feedstocks. The main biotechnological processes yielding bioethanol and biogas from macroalgal polysaccharides are fermentation and anaerobic digestion, respectively.

Since the composition of macroalgae allows for the easier breakdown of the cell wall, macroalgae have also been proposed to produce bio-based pharmaceuticals, nutrients, materials, and fertilizers derived from their pigments, proteins, lipids, and polysaccharides [2]. The species of macroalgae belonging to the genera Ulva, Halimeda, and Codium are the most representative of the green macroalgae (Chlorophyta) used in biorefinery research. These green macroalgae are valuable raw materials as they contain carbohydrates (cellulose and hemicellulose) in 53–70% dry weight, which can be further utilized to produce liquid biofuels (e.g., ethanol). Brown macroalgae (Phaeophyta) include species belonging to the genera Fucus, Laminaria, and Undaria, among others, and their composition includes carbohydrates (e.g., alginates, laminarian, mannitol, cellulose, and fucoidan) in a content of 34–76% dry weight [2]. Brown macroalgae also have more than 20% mineral and ash content (Ca, Na, Mg, and K); for example, Sargassum natans (29%), Laminaria digitata (26.5%), Laminaria japonica (29%), and Undaria (39.3%). This mineral content may need to be removed and further used to produce nutritional products intended for human consumption [21]. Biogas (approximately 60% CH₄ and 40% CO₂) produced from marine macroalgae in the anaerobic digestion process has proven to be a successful source of local power generation despite seasonal variations that may influence the composition of the algal biomass. Companies producing biogas (in the US and Japan) based on these macroalgae have reported methane yields of up to 203 L/kg vs. at 35 °C (Ulva sp.), 280 L/kg vs. at 35 °C (Laminaria hyperborea), and 185.7 L/kg vs. at 50 °C (Laminaria sp.). On the other hand, the production of levulinic acid and polyhydroxyalkanoates (PHA) from macroalgae has been shown to yield up to 6500 USD per ton as reported in 2015, whereas algal lipids are below 1000 USD per ton and bioethanol 823 USD per ton [2,22]. The bioprocesses required to produce bioethanol and biobutanol rely on the effective conversion of polysaccharides into alcohols by different consortia of microorganisms. Regarding the reduction of waste within a biorefinery process, which is an important factor to consider in the CE approach; some studies [23] have demonstrated the feasibility of generating bioelectricity from residues of macroalgae remaining after bioethanol production. The study explored this integrated biorefinery approach of bioethanol and bioelectricity production from three diverse species of macroalgae from the Ghanaian coast: Ulva fasciata (green), Sargassum vulgare (brown macroalgae), and Hydropuntia dentata (red macroalgae). Bioethanol yields of 5.1, 3.7, and 2.4 g per 100 g (dry weight) were obtained from the respective samples, after which the residues were used as substrates in microbial fuel cells (MFC) to generate bioelectricity. The respective power densities obtained were 0.50, 0.46, and 0.48 W/m. Although these results seem promising, a research review regarding sustainable MFC within the CE concept demonstrates that while the inclusion of MFC to generate electricity may include research with microalgae (e.g., Chlorella sp.), there are no advances in trying to generate bioelectricity from bioethanol or biogas waste from biorefineries. Approaches including MFC technology in the CE model may offer more advantages in industrial and municipal wastewater treatments [24].

Researchers continue to conduct projects aimed at optimizing biotechnological approaches for producing mainly biofuels, which is one of the most interesting avenues for establishing biorefineries within the circular economy (CE) model; for example, bioethanol, with an estimated increase of 6–8% per year by 2050 [2,25]. According to the literature reports, by 2021, the number of patents related to the macroalgae biofuel market was 40. The number of patents related to some aspects of the production of bioethanol, biofertilizers, levulinic and formic acids, agar, cellulose, lipids, and pigments mainly from brown macroalgae was 7. These patents were granted and/or applied for in Australia, the US, India, Spain, The Netherlands, and China [2]. Funding for projects related to macroalgal-based biorefineries (≥£/USD 100,000 per project) over periods of 2–4 years and granted between 2018 and 2022 in Australia, the UK, the USA, Norway, France, and New Zealand demonstrates, along with registered intellectual property, that interest in biorefineries as an approach to the CE model is of utmost importance, although it is still in its infancy, since biotechnology in this area has yet to surpass the laboratory or pilot scale to be fully installed on an industrial scale. Collaborative projects (academics and industry) related to macroalgae-based biorefinery (2019–2023) demonstrate continuous interest in approaching the CE model. An example is the collaborative project to develop a sustainable Integrated Multi-Trophic Aquaculture (IMTA) model that supports commercial seaweed production (Australian Government, Department of Industry, Science, Energy, and Resources Cooperative Research Centres Program Fisheries Research and Development Corporation, total project value USD 4,097,309). Another one involves the transformation of the costly mussel-industry pest seaweed species Undaria pinnatifida into a sustainable, high-value agricultural product for the global market (New Zealand, New Zealand Ministry of Primary Industry’s (MPI) Sustainable Food and Fibre Futures program, total project value USD 52,584). In collaboration with Riddet Institute, University of Auckland, Plant and Food Research, Singapore’s Agency for Science, Technology, and Research, Singapore Institute of Food and Biotechnology Innovation, and industry partners Wakatu Incorporation and Te Runanga o Ngai Tahu, the project seeks to investigate how the red seaweed Karengo and the microalga Chlorella could become everyday alternative sources of protein (New Zealand, New Zealand Ministry of Business, Innovation and Employment Catalyst: Strategic—New Zealand—Singapore Future Foods Research Programme, total project value USD 2,103,375) [2].

Regarding CE biogas-based plants, a study published in 2025 [26] investigated the design, techno-economic, and life cycle assessment of CE-based biogas plants (capacity of 300 t/day) in rural and urban areas in India, using rural waste like rice straw and cattle dung and urban waste like sewage sludge and municipal solid waste. The study indicated that the rural and urban frameworks show positive net present values (NPV) of 3.24 million USD and 1.30 million USD, respectively. In this plant, the biogas is upgraded by membrane separation to enrich in methane. The higher NPV of the rural framework is due to higher total solids in the rural biogas plant, resulting in higher amounts of compressed biomethane gas (CBG) and fertilizers. The higher capital costs are due to the membranes and anaerobic digesters, and the higher operation costs are due to labor and feedstock prices. The life cycle assessment (LCA) results show that the total climate change impact (kg CO₂ eq./kg CBG) of rural and urban frameworks are 4.87 and 4.52, respectively. Higher compost quantity, feedstock transportation, methane leakage from membrane separation, and combined heat and power plant (CHP) emissions are responsible for higher emissions in rural areas. It is important to mention that the emission distribution showed that leakages from the biogas plant contribute the most to climate change (63.65% and 68.36%), followed by composting (19.5% and 17%) and membrane separation (7.8% and 7.3%) for rural and urban biogas plants, respectively [26]. The implementation of this plant presented other local advantages such as improvement of local health due to lower water and air pollution, decreased waste and odor, increased local job creation, clean energy availability, and promotion of organic farming, social equity, and community development. Thus, this is an example of current implementation of CE-based frameworks for biogas plants in rural and urban parts of India to make them economically and environmentally sustainable. The authors noted that a biogas plant at large presents some issues that need to be addressed, such as the stability of the anaerobic digester, the carbon to nitrogen (C/N) ratio, steady supply of feedstock, maintaining quality, and dealing with fluctuations in feedstock prices as well as the construction of systems of pipelines for gas distribution. They mentioned that in India, the first biogas-based power plant fed with rice straw (Fazilka district of Punjab, India) uses 10 t/d of rice straw to produce 6000 kWh of electricity but is operating below its higher capacity (40 t/d) due to technical issues related to the instability of the anaerobic digester and the C/N ratio supply, which leads to a lower biogas production rate. Thus, it is recommended that co-digestion of rice straw and cattle dung is carried out to maintain the good performance of the plant [26].

To motivate the implementation of CE in industries, Schilling and Weiss [27] discussed the importance of biotechnology for a transition to CE. First, they suggest the utilization of more renewables (e.g., biomass, sugars, sunlight, and CO₂). Second, they recommend using bio-based feedstocks to generate new, better materials (e.g., polytrimethylene terephthalate (PTT) from DuPont and polylactic acid (PLA) from Natureworks, as well as polymers from Novamont, which are examples of companies committed to sustainability). Third, they suggest designing for improved lifecycles, considering the durability and degradation of materials. Fourth, they emphasize aiming towards compostable products, ensuring that the desirable biological processes contribute to degradation and environmental preservation. This composting may require novel enzymes and microorganisms, such as for the biodegradation of polyethylene terephthalate (PET). Fifth, they suggest improving reuse and upcycling at the end of a product’s life. In this last suggestion, the authors describe the example of Lanzatech, which uses a biological system to consume synthesis gas as a source of carbon and energy for making ethanol and aims to produce more chemicals based on biological processes soon. Thus, biotechnology plays a crucial role in facilitating more sustainable processes that align with CE principles. Schilling and Weiss [27] proposed a scheme (Figure 1) featuring biotechnology at the core of moving towards CE. In summary, CE implementation is advancing, and the positive impacts on the environment and industry are relevant, but so far it is constrained to a small group of countries that have incorporated CE implementation into their regulations, thus investing in research and technology to expand the incorporation of communities into the culture of sustainability.

3. Design Methodology and Process Simulation: Process Design and Process Scale Up

CE as a consumption/production model for supplying goods and services to a society also points to the need for engineering methods and resources to plan and evaluate the feasibility of bioprocesses involved in the transition from linear to circular economy. Thus, as in other process engineering applications, designing or technical improvement of CE bioprocesses needs a systemic methodology aimed at attaining the best projection in process performance technically, economically, and concerning its environmental impact Figure 2.

Process engineering has developed different methodology approaches for process and bioprocess synthesis and design, all of them searching for the feasibility assessment of a project regarding a defined optimization goal with the lowest possible uncertainty. Classically, process development begins with conceptual ideas issued from convergent social needs and business opportunities. Then, lab experiments ought to demonstrate feasible possibilities that might continue into process development in a path depicted by the Technology Readiness Level (TRL) system, in which process design methods are required. Results from the design methodology allow for a decision-making process on bioprocess engineering projects. Although there is not a generally valid or unique process design method, there are some recurring features classically developed in each process or bioprocess design project, such as the stages shown in the Conceptual design of chemical processes [28], as follows:

• Reaction and information structures (batch or continuous);
• Input–output structures in the process flowsheet (mass and energy balances);
• Recycling structure in the flowsheet;
• Focus on the separation system (downstream);
• Design of the energy network (heat-exchange interconnections);
• Cost diagrams and alternative process scenarios.

Other methodologies have been established [29], and, more recently, specific approaches to bioprocess design have been undertaken [30,31]. Design strategies have evolved concomitantly with the development of numerical methods, informatics, and computing power. Nowadays, digital simulators are available which allow preliminary and complete process design by performing iterations within the design process in a much easier way. In the biotechnology sector, using process simulators is an expanding trend for technological development and the translation of processes between scales. In fact, the conceptual design of any process or bioprocess can be considered as a work-in-progress digital flowsheet in simulation environments with recursive incorporation of engineering criteria and data [32] within changing scenarios, where mass-and-energy-balance calculation results are available in each iteration through simulation algorithms, as much as economic and environmental data form the different versions of the process or bioprocess being designed [33]. This flexibility provided by process simulation has expanded process design limits going from designing a new process or bioprocess, to cost analysis for new or existing processes, production planning and scheduling, cycle time reduction and debottlenecking [34], assessing environmental impact and estimating life cycle parameters, weighing pollution prevention and control, enabling technology transfer, performing risk assessment [35], and developing CE processes, as stated before.

The continuously growing knowledge of physical principles underlying unitary processes involved in materials processing or bioprocessing from basic resources until final products has strengthened process simulation. For example, in biotechnological processing, there are impressive advances in biocatalysis going from cellular systems including mammalian cultures to cell-free systems for complete synthesis of bioactive compounds, to robust synthetic microbial consortia technologies for pollution control or CE bioprocesses. Likewise, in downstream processing, important technological progress is documented now on membrane and chromatographic unitary processes and combinations of both, assuring efficient separation for practically any bioactive molecule. Thus, the new theoretical knowledge gained from research on these unitary processes provides a thorough basis for process modeling and simulation.

In process engineering, collecting techno-economic data in an engineering project either for a new process or bioprocess synthesis or for the techno-economic improvement of an existing one would accelerate economic and social value creation and new products’ availability under eco-friendly or CE systems. Process design methodologies based on digital simulation provide decision-making elements and responses in the shortest possible time for on-going process or bioprocess synthesis. These might be some of the reasons for the increasing applications of process simulators for techno-economic feasibility assessment, mostly in environmental engineering, biopharmaceutical processing, the food industry, and technical consulting on processes or bioprocesses.

3.1. Identifying Techno-Economic Challenges in Large Bioprocess Design

Whatever the chosen methodological approach to process synthesis in biotech, process design often involves complex technical tasks and economic constraints that define process feasibility. Some of the more important techno-economic factors to address in successful process design are as follows:

(i)

Biocatalyst physiological stability [36,37]. Biological catalysts such as enzymes and microbial, vegetal, or animal cells are strongly dependent on culture conditions that must be adjusted to keep cellular physiological activity in the optimal production of a bioactive molecule. These culture conditions are operational, meaning they are rather controlled by strategies directed to pre-established optimal objectives. Normally, microbial strains or vegetal and animal cellular lines are genetically stable through some generations under proper preservation methods, but eventually, unknown genetic control circuits could alter the expected physiological behavior of the cells, thus producing a reduction in productivity. These aspects should lead to frequent stability assessments in the lab and to checking up on the cellular lines’ kinetic behavior and titer measurements, to confirm productivity data for bioprocess simulation.

(ii)

Preserving the biocatalyst production yields between scales [38]. Stoichiometric yields and volumetric productivities from cellular lines and microbial strains are usually maximized in laboratory-scale ideal mixing bioreactors, but in large-scale industrial bioreactors, where mixing heterogeneities often appear, yields and productivities are often lower [39]. This is still an open problem in bioreaction engineering, so the projected amount of the product in industrial bioprocesses must consider the yield loss due to changing scale, as to reach annual production goals. Thus, an actual challenging task is to develop an accurate way to estimate the yield decrease in large bioreactors through what is called the scale-down approach. Scaled-up processes need to be simulated considering the proper adjustments in stoichiometric reactions to deliver good data for the decision-making process.

(iii)

Concentration heterogeneities in large bioreactors [40]. Achieving perfect homogeneity in a large bioreactor would be an impossible task with the actual mixing technology, meaning that cells will alternate between rich substrate and oxygen zones, and poorly mixed zones with scarce nutrient availability in the bioreactor. The yield loss stated before is a result of this oscillating culture conditions in large heterogeneously mixed bioreactors.

(iv)

Oxygen transfer for aerobic processes [41]. Low solubility of oxygen leads to an eventual culture growth limitation by dissolved oxygen exhaustion in the culture medium for aerobic processes, even with continuous air supply for interphase oxygen transfer in the bioreactor. In laboratory scale bioreactors, oxygen transfer rate and then oxygen availability can be improved by increasing the bioreactor stirring speed or the airflow input, which are unfeasible strategies in large bioreactors. Consequently, the culture switches from carbon-limited to oxygen-limited, meaning the growth kinetics would present a linear behavior for growth and bioactive molecule production with respect to culture time, so no exponential production can be expected. These linear-like kinetics should then be considered in scale-up or scale-down procedures and process simulations.

(v)

Recovery factors and efficiencies in large-scale downstream equipment. Mass and energy balances in unitary processes throughout the downstream processing could be affected [42] by equipment efficiencies associated with hydrodynamics in specific geometric equipment designs or mass or heat transfer limitations. So, some unitary processes such as distillation, liquid and solid extraction, spray drying, or chromatographic processes need to be scaled up in pilot plant facilities to generate data for more accurate simulations, leading to better large-scale unitary process performance.

(vi)

Excessive water and energy consumption. Water and energy consumption are important but often neglected factors in process scale-up. In designing or operating an industrial-scale process, there must always be a strategy for reducing water and energy consumption. Occasionally, if large amounts of water or energy are needed and those are increasing with the process scale, scale-up projects could result in unfeasibility. Water recycling, more efficient use of water, or water recovery by treatment plants should be combined in a strategy for a scalable lower-environmental-impact alternative. Process simulations are, of course, useful in this regard.

(vii)

Large environmental impact or irreversible ecological damage [43]. Potential or actual pollution in water, air, and soil in ecosystems should be kept at minimum levels. This can be evaluated from simulated waste and emission estimations in the bioprocess. Under no circumstances should a production process be considered feasible if high pollution degrees or irreversible damage to the environment exist. Environmental protection technologies must be incorporated to scale-up estimations.

(viii)

Accurate cost estimates both for investment and process operation [44]. Process simulation delivers costs and financial indicators that report economic feasibility for scaled-up processes. Precise and accurate figures are important for scaling up decision-making process. Thus, estimating costs and financial indicators needs exact economic data that might be challenging to obtain.

(ix)

Raw material availability. Industrial production requires sufficiency of raw materials in accordance with production scheduling on a yearly basis. This is an indirect scaling factor that is not related to other technical difficulties during process scale up, but that still represents an important challenge that should be considered in scale up calculations.

These process complexities must ideally be taken into consideration in process design as much as possible, particularly when the design of the process is developed in digital simulation environments. Process simulations would be as accurate as the modeling of the process behavior while changing the scale. Thus, theoretical knowledge of the process and experimental data from bench and pilot plant equipment have the utmost importance in process design.

3.2. Lab-Scale to Large-Scale Transition of Bioprocesses Through Assisted Simulation with Bench or Pilot Tests

Addressing scaling problems for industrial production of biologicals has been approached in chemical and biochemical engineering with two basic strategies, upward scaling and scale down (See Figure 3). On the one hand, in upward scaling or scaling up, the laboratory results are useful to advance the knowledge on the process to middle scale tests dubbed pilot plant trials [45], where key unitary processes, such as distillation, liquid extraction, or chromatographic processes, are designed aiming at the actual large capacity equipment for the estimated final industrial production. Nevertheless, using pilot plant results in larger process calculations could lack accuracy, due mostly to the highly non-linear behavior found between scales for cellular processes and other unitary processes for bioseparation. Thus, results from pilot plant tests are completed with process simulations that allow for a more accurate projection of the process on a required larger scale. However, the construction of pilot plant facilities might be expansive enough to importantly impact on the economic feasibility analysis of the project.

On the other hand, scale-down methodologies [45] aim to replicate problems identified in industrial-scale equipment at the laboratory level, to find out solving strategies in the lab that afterwards will ideally be transferred back to the larger-scale equipment. The scale-down experimentations can be completed with thorough process simulation that might include Monte Carlo-based sensitivity analysis. A drawback of the scale-down approach is that reproducing large-scale behavior at lab scale needs some validating assessment on process behavior equivalence between scales, which is still a major concern in biochemical engineering scale-up.

Although a complete process simulation is of great assistance in biotechnology projects, a significant improvement of the outcome would be provided by experimental data from laboratory, bench, and pilot facilities, and vice versa. Thus, for large production projects, the best practice is to use pilot plant experimental results and directly measured physical properties of compounds into large-scale projected simulations with stochastic complementary sensitivity analysis. There is a relatively scarce number of studies combining pilot plant results with large-scale simulations, though. In recent work [46], a thorough approach for downstream processing has been used for scaling and process evaluation. In contrast, there are abundant contributions related to pure simulation projections and studies focusing on biofuels production, water treatment, anaerobic digestion, and CE applications, on the one hand, and manufacturing optimization in biopharmaceuticals on the other [35].

Transferring a process from a basic development in the laboratory to production-level scale is a complex operation that often requires multiple experimental verifications and process validations through calculation. In any case, developing a process from lab to industrial production still presents great challenges to bioprocess engineering, which are better approached by combined experimental and theoretical simulation methods [47,48].

4. Opportunity Areas with SuperPro Designer for Implementing Technical and Economic Assessments of Bioprocesses

The implementation of large-scale strategies for waste management, such as anaerobic digestion, among others, requires economic evaluation, considering the specific technical and geopolitical conditions. Techno-economic analyses are essential for determining project feasibility by combining process modeling, engineering design, and economic evaluation. Excel 365 (Microsoft, Redmond, Washington, DC, USA), SuperPro Designer v.14 (Intelligen, Inc., Scoth Plains, NJ, USA), ASPEN plus V12 (Aspen Technology, Inc., Bedford, MA, USA), and BioSTEAM 2 (BioSTEAM Development Group, Urbana, IL, USA) are common digital tools for techno-economic analyses during the development and optimization stages. However, Excel is limited by its spreadsheet-based approach, making it challenging for non-experts. BioSTEAM is an open-source process simulation software that is specifically designed for biochemical and bioprocess applications. It is free to use and its interface is simpler than SuperPro Designer or ASPEN, providing an accessible platform that facilitates design, simulation, and techno-economic analysis of biorefineries and bio-manufacturing processes. The software’s open source allows for high customizability and flexibility, enabling users with programming skills to modify and extend its functionalities to meet specific needs. While it may not have the extensive integration capabilities of ASPEN or SuperPro Designer, BioSTEAM can still connect with other open-source tools and databases, making it a versatile option for research and development in the bioprocessing industry. ASPEN Plus, as the leading commercial process simulation software, is renowned for its extensive capabilities across multiple industries including chemicals, petrochemicals, power, and pharmaceuticals. ASPEN Plus offers a robust and comprehensive interface that supports detailed customizations, making it particularly attractive to advanced users and industry experts. However, ASPEN Plus was first designed for being used in continuous processes and complex chemical engineering simulations, and it is also the most expensive option among process simulators. On the other hand, SuperPro Designer is specifically developed for bioprocess simulation, handling both batch and continuous operations. It offers user-friendly features for easy implementation by non-experts, advanced material and energy balance calculations, extensive databases, and thorough process economics. SuperPro Designer is particularly advantageous for techno-economic analyses evaluation during technology development and optimization, making it a preferable choice over ASPEN Plus for large-scale bioprocess feasibility studies [49]. Comparing the use of SuperPro Designer against Aspen Plus, van Rijn et al. [50] found that, during the evaluation of the techno-economic aspects of cellulosic ethanol production, both software packages produced ethanol production costs that differed by only 10%, demonstrating substantial agreement in their modeling approaches. However, SuperPro Designer estimated a significantly lower total capital investment at USD 125 million compared to the estimation performed by Aspen Plus of USD 180 million. This difference is probably originated in the capital cost factors (commonly Langley factors) used during the design of the plant; these factors can be varied in a high range, for instance the typical installation factor value is 0.5 and can be varied within a range of 0.2 to 1.5 times the value of purchase-cost of equipment [51]. Additionally, the SuperPro Designer model revealed that continuous fermentation is more economical than batch fermentation due to decreased reactor volume and reduced downtime. Most notably, SuperPro Designer’s model indicated a nearly 25% reduction in ethanol production costs when increasing the scale of the biorefinery. These findings suggest that SuperPro Designer offers a more cost-effective solution for modeling and scaling up biorefinery operations, making it a preferred choice for stakeholders aiming to optimize economic and operational efficiencies in bioethanol production.

An important aspect of simulating bioprocesses is the integration of batch mode operation that accounts for time-dependency and a sequence of events that is the typical operation used during bioprocessing. The first Batch process simulator, BATCHES (Datastream Systems, Inc., Greenville, SC, USA), developed in the mid-1980s, allowed for the integration of differential equations over time, thereby modeling the dynamic nature of batch processes. With advancements in simulation technology, tools like Aspen Batch Process Developer (ABPD) (Aspen Technology, Inc., Bedford, MA, USA) and SuperPro Designer emerged, focusing on recipe-driven and bioprocessing applications [23]. Nevertheless, some works have claimed for a combined use of several simulators when experimental data are not fully available. For instance, an interesting study was performed by Rouf et al. [52], regarding the combined use of ABPD and SuperPro designer to simulate and compare the use of serum and serum-free medium to produce 11 kg of purified tissue plasminogen activator (t-PA) from Chinese Hamster Ovary (CHO) cells. The study indicated that SuperPro Designer has significant advantages in modeling complex biotechnological applications, such as dual-step cell culture growth and purification stages, due to its specialized features for bioprocesses. However, the input information for ABPD and SuperPro Designer simulations differ due to their distinct unit operation models. ABPD primarily uses data available in the literature or estimated from built-in correlations. In contrast, some SuperPro Designer simulations, like those for chromatographic columns, require experimental data for parameters such as binding percentage and recovery rates to perform accurate simulations. In the work of Rouf et al., ABPD was employed to simulate the affinity chromatography and ultrafiltration steps. The outputs from ABPD were then used as inputs for further simulation in SuperPro Designer to model the entire bioprocess comprehensively. Finally, SuperPro Designer was also employed for the economic evaluation of the process. While Aspen Batch Process includes an economic evaluator, it is more suited for chemical processes, whereas SuperPro Designer evaluator is specifically designed for bioprocesses, making it more suitable for this type of application. The analysis concluded that SuperPro Designer could be used to provide a detailed economic evaluation including capital and operating costs that results in a solution with a higher return on investment when using serum-free medium characterized by a ROI of 115%, a gross margin of 47%, and net profit of 49.86 million USD [52]. Finally, it is worth mentioning that there is not a single article, guide, or document highlighting the technical and specific differences between ASPEN Plus and SuperPro Designer for bioprocess modeling. Such information would be valuable in providing a more robust comparison between both software.

The SuperPro Designer software allows for the performance of mass and energy balances over a flow diagram process specified for a given process. These balances are necessary to match the specifications of the equipment required to carry out a specific unitary operation. The software includes several types of equipment for a wide variety of unitary operations grouped into 26 main categories called procedures. These procedures include batch vessel procedures for reactions (such as various types of bioreactors), continuous reaction (including environmental procedures such as aerobic and anaerobic biodigesters), inoculum preparation, filtration, centrifugation, homogenization/milling, chromatography/adsorption, drying, sedimentation, phase change, distillation, and absorption/stripping, to mention the most important ones. Once the flow diagram process has been set and the mass and energy balances are done, the next step is the economic and financial analysis based either on the database of SuperPro or with specific cost determined by the users. A comprehensive description of the default project parameters, cost calculations, and investment details is available within various menus of the SuperPro Designer software. To enhance understanding and appreciation of these characteristics, Figure 4 provides a detailed summary of these default values. This figure visually breaks down the different cost components, including direct costs, indirect costs, and other associated expenditures, making it easier to grasp the overall financial structure of a project. By offering a concise yet thorough overview, Figure 4 allows users to quickly comprehend the critical financial aspects and default settings utilized in SuperPro Designer, facilitating more informed decision-making and project planning.

SuperPro Designer software also offers a comprehensive suite of financial indicators to evaluate the economic viability of a given project. These indicators help users make informed decisions regarding project investments and forecasting. Among the key financial metrics calculated by SuperPro Designer are the Net Present Value (NPV), Internal Rate of Return (IRR), Gross Profit, Net Profit, Return on Investment (ROI), Payback Time, and Gross Margin. Figure 5 provides a detailed illustration of these calculations.

One of the first stages to simulate a process in SuperPro designer is defining compounds and mixtures accurately, and if a new compound needs to be entered, it is advisable to use the most similar existing compound as a template. For instance, Michailos et al. [53] describe the use of sugar cane bagasse to produce gasoline and butanol. Sugar cane was simulated as a mixture containing cellulose, hemicellulose, and lignin; it was assumed that cellulose and hemicellulose consist only of glucan and xylan, respectively, which are present in the database of SuperPro. Following a similar approach, several yard trimming residues can be simulated with some extra components such as proteins and lipids [54]. Therefore, it is of paramount importance to perform data analysis and identification of all the components involved in a process prior to performing a given simulation.

During the use of SuperPro Designer for correctly assessing different types of bioprocesses, it is essential first to understand all stages of the process at both laboratory and industrial levels. For instance, most of the process simulated using solid residues involves the collection and preprocessing of waste, washing to remove dust and inorganic materials, and pre- and size reduction either by shredding or milling. Subsequently, each stage of the process should be implemented using appropriate unitary operations. Selecting optimal equipment for each unitary operation is crucial, as well as determining the operating conditions for each piece of equipment to ensure efficiency. Despite the higher amount of equipment in SuperPro, there is some equipment for unitary operations that should be modeled in special forms. For instance, a unitary operation of ultrasound needs to be modeled using storage recipes and an adequate energy input to simulate the high demanding energy equipment of ultrasound [55,56]. Ultrasound is a superior method for extracting crude anthocyanins from Hibiscus sabdariffa calyx. It reduces solvent use, energy consumption, extraction times, and increases yields compared to heat-assisted extraction. Ultrasound also preserves compound quality, ensuring a safer, higher-quality product. Despite higher initial costs, ultrasound achieves more cost-effective production, making it ideal for industrial applications [55]. Regarding the production of flour from some sweet potatoes, ultrasound enhances drying efficiency, reduces drying times, and preserves the nutritional quality of the sweet potato. By breaking down cell walls, it facilitates better moisture removal and retention of essential nutrients. Despite the initial equipment costs, ultrasound leads to a more cost-effective production process through reduced energy consumption and improved product quality. This makes it a highly valuable method for industrial applications focused on producing high-quality, nutrient-rich sweet potato flour [56]. Another important unitary operation that lacks specific equipment in SuperPro Designer is pyrolysis. Consequently, furnace equipment is not included in the database of SuperPro Designer. Biomass pyrolysis is essential for producing industrial bio-products through thermal decomposition of biomass without oxygen. This process yields biochar, bio-oil, and biogas, with product distribution influenced by biomass components: cellulose, hemicellulose, and lignin. Pyrolysis offers a renewable energy source and enhanced sustainable industrial production. Economic evaluations show that biomass composition impacts costs and revenues, making this method viable and efficient for valuable bio-product production [57]. In specific works, the biomass pyrolysis section consists of a stirred reactor, a plug flow reactor, and associated auxiliary equipment, including a component splitter, a mixer, and a heat exchanger. In the subsequent product separation and collection section, char is separated, and vapors are condensed to recover bio-oil using a gas cyclone and a condenser, respectively [58].

Cost data for utilities such as water, energy, steam, cooling water, and others should be entered accurately, along with cost data for equipment, labor, raw materials, and both main and secondary products. This cost information is vital for economic analysis and optimization.

Finally, defining the sequence and scheduling of unitary operations ensures that the process runs easily and efficiently. SuperPro Designer’s capabilities in process diagram optimization, cost analysis, and scheduling make it an invaluable tool for developing efficient bioprocesses using industrial waste. Another specific tool of SuperPro Designer is the identification of bottlenecks of the process. For instance, during the production of microbial oil and carotenoids, Villegas-Mendez et al. [59] identified a significant bottleneck in the fermentation step, which had the longest occupancy time. By adding extra fermenters (bioreactors) in a staggered mode, the cycle time was reduced, and the number of batches per year increased. This adjustment improved the process’s economic viability, resulting in shorter payback time (passing from 10 to 5.78 years) and higher return on investment (ROI) (passing from 10 to 17.3%).

Developing multiple scenarios in SuperPro Designer allows users to explore various configurations of processes to optimize and improve bioprocesses. By simulating different setups, users can identify the most efficient and cost-effective methods for scaling up a given bioprocess. For instance, Czinkóczky and Németh [60] explore the development of multiple scenarios using SuperPro Designer to optimize the production of surfactin and lichenysin via Bacillus fermentation. The techno-economic simulations demonstrated the benefits of equipment sharing and foam separation in reducing capital (51 to 53% or reduction) and operational costs (50 to 52% of reduction). Sensitivity analyses revealed that facility-dependent costs significantly impact economic indicators, while equipment sharing substantially lowers investment costs for small- and medium-sized enterprises. The use of the multiple-scenarios approach is particularly useful in understanding the potential impacts of scaling up, such as changes in yield, production rates, and resource utilization. It also helps in assessing the feasibility and robustness of the process under different conditions, ultimately leading to better decision-making and planning for industrial-scale production. Using a multiple-scenarios approach in SuperPro Designer and Response Surface Methodology, researchers in the study by Bakari et al. [61] were able to understand the potential impacts of scaling up the pyrolysis process of rice husk. This method allows for the evaluation of different configurations by varying reaction temperature and residence time, ranging from 350 to 800 °C and 0.25 to 60 s, respectively. The flexibility of this approach enabled the assessment of changes in yield, production rates, and resource utilization, proving invaluable in determining the feasibility and robustness of the process under different conditions. Consequently, it facilitated better decision-making and more efficient planning for potential industrial-scale applications.

4.1. Using SuperPro Designer for Providing Valuable Data for Decision-Making

The application of techno-economic analysis using SuperPro Designer to study bioprocesses related to the CE has led to the publication of several works. Some of their main characteristics are displayed in

Table 1. Characteristics and sensitivity analysis variables of various bioprocesses modeled using SuperPro Designer.

Reference	Target Biomolecule	Raw Material	Brief Description of the Simulated Process in SuperPro	Size of the Plant	Variables Studied During the Sensitivity Analysis
[50]	Bioethanol	Sugarcane bagasse and sweet sorghum	Biomass collection Bagasse preparation Pre-treatment Screw press Culture propagation Liquefaction Fermentation Downstream processing	300,000 MT/year of sugarcane dry matter or sweet sorghum bagasse	Enzyme dosage Conversion efficiency Solids loading Biomass throughput
[54]	Compost and biogas	Yard trimmings	Material preparation Material mixing Anaerobic digestion Biogas utilization Digestate utilization	Capacity of 20,000 MT/year	Plant size Total solids (TS) content and organic components of materials Retention time Degradation efficiency Heat recovery Boiler efficiency Tipping fees By-product/compost price Drying temperature Percentage of compost sold in bags
[62]	Biochar	Date palm waste	Feedstock preparation Pyrolysis reaction Heat recovery Biochar collection By-product gas and liquid management	20,000 MT/year	Plant size Feedstock cost Biochar yield Energy input requirements Market price of biochar Operational efficiency
[63]	Bioethanol	Sweet potato	Reception and washing Crushing Drying and milling Gelatinization and liquefaction Simultaneous saccharification and fermentation. Ethanol recovery and dehydration Product storage.	130 t/h sweet potato with an annual production of 90,000 m3 anhydrous ethanol	Dry matter ratio of sweet potato to water: 0.13 to 0.50 kg dry matter/kg total water. Fermentation efficiency: 70% to 92%. Sweet potato sugar content: 69% to 82%. Final humidity of sweet potato flour: 8% to 20%.
[64]	Succinic acid	Bakery wastes	Grinding of bakery wastes Blending in process water and enzymatic hydrolysis. Fermentation using Actinobacillus succinogenes Solid–liquid separation and adsorption of impurities. Distillation and crystallization Drying	The treatment plant was designed to process 1 t/day of bakery waste	Quantity of bakery waste treated. Utility costs. Succinic acid price. Variables were varied within ±20%.
[65]	Biodiesel and glycerol as a by-product	Wastewater sludge	Sludge drying Sludge grinding Lipid extraction Transesterification Biodiesel purification Cultivation of oleaginous microorganisms using sludge Biomass harvesting Biomass drying Lipid extraction from microorganisms 2nd Transesterification Biodiesel and glycerol purification	The treatment plant was designed to process 260 t/day of dry sludge	The plant scale (80 to 440 t/day of sludge) Lipid content in the biomass Lipid productivity The necessity of sterilization in the fermentation process
[66]	Biojet fuel, specifically farnesane,	Sugarcane bagasse	Bagasse reception and pretreatment Cellulose hydrolysis Sugars fermentation to farnesene Product recovery Farnesene upgrading to jet fuel Combined heat and power (CHP) unit	The simulated treatment plant was designed to handle a capacity of 100 t/h of dry raw bagasse	Sugars conversion: 12% to 22% Natural gas utilization (for steam reforming hydrogen production): 350 kg/h to 850 kg/h Urea consumption (fertilizer): 80 kg/ha to 160 kg/ha
[67]	Primary final product: butanol. By-products: acetone and ethanol (during ABE fermentation)	Corn stover	Pre-treatment of corn stover Enzymatic hydrolysis Fermentation using Clostridium species to produce ABE (acetone, butanol, ethanol) Downstream processing to separate and purify butanol, acetone, and ethanol Waste treatment and by-product management	The treatment plant simulated processes around 1000 MT/day of dry corn stover	Feedstock cost (ranging between 30 to 70 USD per dry metric ton) Enzyme loading rates and costs Fermentation yield and efficiency Capital costs and plant scale-up scenarios Operating costs, including labor, utilities, and maintenance
[68]	A modified acrylic bio-adhesive	Glycerol (by-product of biodiesel)	Producing acrylated glycerin (AG) via Fischer esterification of acrylic acid and bio-based glycerol. Production of the chain transfer agent (CTA). RAFT (Reversible Addition-Fragmentation Chain Transfer) polymerization of AG with CTA and azobisisobutyronitrile (AIBN) to produce polyacrylated glycerin. Precipitating the polymerized product to obtain the final liquid bio-adhesive.	Simulated plant scales: 1 t/d, 2 t/d, 5 t/d, 10 t/d, and 40 t/d	Total capital investment: +50% to −30%. Bio-adhesive selling price: ±50%. Comparison of glycerol sources: biodiesel by-product vs. crude oil-derived. Key factors in production cost: materials, labor, capital, and catalyst price.
[53]	Using sugar cane bagasse to produce methanol to gasoline (MTG) and butanol	Cane bagasse	Bagasse pre-treatment (crushing and drying) Gasification and syngas cleaning (for MTG process) Methanol synthesis and conversion to gasoline Cellulose hydrolysis and fermentation (for butanol process) Distillation and product recovery (for butanol process) Power generation using combined heat and power units	The simulated treatment plant processes 100 MT/h of bagasse	Technical parameters: enzyme loading and sugar conversion rates for butanol production. Catalysts cost and recycling rates for the MTG process. Parameter variations range: ±10% to ±30%
[69]	crystallized caffeine and powdered catechins	Black tea waste	Collection and transport of black tea waste. Leaching with hot water. Clarification and filtration. Differential extraction using dichloromethane (DCM) for caffeine or ethyl acetate (EA) for catechins. Evaporation, crystallization, and drying of caffeine and catechins.	The size of the treatment plant simulated processes 30,000 metric tons (MT) of black tea waste per year.	Leaching yield. Recovery yield of caffeine and catechins. Crystallization yield. Vaporization fraction during evaporation. The ranges for these variables were adjusted for suitable conditions based on feasibility assessments.
[70]	Malic acid	Crude glycerol derived from waste cooking oil	Preparation of fermentation media Seed fermentation Production fermentation In situ recovery Purification of malic acid	Production capacity of 510 t/year of malic acid	Direct fixed costs Plant volume Equipment purchase cost Crude glycerol cost Energy consumption. Variable ranges analyzed: ±20%
[59]	Microbial oil and carotenoids	Brewer’s spent grain, pasta processing waste and bakery waste	Fermentation Biomass recovery Separation of carotenoids and microbial oil	Working volume of 1500 L in the production bioreactors	Sensitivity analysis variables: microbial oil (MO) price and batch throughput MO price ranges: 1–4 USD/g Batch throughput ranges: 5 g to 5 kg per batch

. As shown in Table 1, a wide variety of wastes have been successfully studied as raw materials to produce biofuels and chemicals, modeled to produce large quantities of products, typically on the order of metric tons (MT). In all the studies presented in Table 1, there is a common stage of pretreatment of raw materials before further processing, which is necessary due to the nature of the raw materials. Additionally, most of the processes emphasize the stages of material preparation, pretreatment, conversion or reaction, bioproduct recovery, and by-product management. In the context of sensitivity analysis performed using SuperPro Designer, several key factors are scrutinized to optimize and ensure the economic feasibility and efficiency of bioprocesses, as outlined in Table 1. These factors include the efficiency and yield of the processes, which encompasses the analysis of conversion efficiency, fermentation efficiency, degradation efficiency, and the overall yield of the desired product. Numerous studies aim to optimize these parameters to improve the economic perspective of the target biomolecules or biofuels produced during the simulations. Additionally, various cost factors such as feedstock cost, enzyme loading costs (if applicable), capital expenditure, and operational costs (including labor, utilities, energy, and maintenance) are commonly analyzed during the sensitivity analysis of the simulated scenarios. The size of the plant and its throughput capacity are crucial factors that significantly influence the scalability and economic viability of the processes. Several studies have conducted sensitivity analyses across a wide range of production sizes, making it one of the most common analyses reported. Moreover, variables like the solid loading of materials, energy input requirements, utility costs, and heat recovery are often examined. Optimization of these parameters can result in more robust processes that support variability in inputs such as raw material quality and composition, which is useful for analyzing operational costs. Finally, evaluating the market prices of final products is important for assessing the potential profitability and economic sustainability of the processes, as well as to align with commonly established market prices of the desired products.

The efficient use of SuperPro Designer in various analyses for the development of bioprocesses aimed at the production of fuels and industrially significant molecules from different types of industrial waste involves several critical steps that are widely used in several publications. For example, Giwa et al. [62] utilized SuperPro Designer v8.5 to design and simulate a system for the pyrolysis of date palm waste to biochar using concentrated solar thermal energy. This study highlighted the economic and environmental sustainability implications of this process by benchmarking it against conventional electric heating-based pyrolysis. With the use of SuperPro, this study indicated that the solar-based process was more economically viable with attractive financial indicators such as payback time (PBT) of 4 years and 132 days, an internal rate of return (IRR) of 14.8%, a return on investment (ROI) of 22.9%, and a gross margin of 35.5%. Furthermore, the environmental impact assessment revealed that CO₂ emissions from the solar energy-based pyrolysis accounted for only 38% of those from the conventional process, demonstrating its environmental friendliness. The study concluded that the solar concentration technique is economically viable, environmentally friendly, and presents a sustainable opportunity for biochar production while reducing life-cycle emissions and costs.

The use of Super Pro for studying different approaches to treat solid residues can significantly enhance a decision-making process. An example is the techno-economic feasibility of comparisons of solid-state anaerobic digestion (SS-AD) against composting of yard trimmings [54]. Both systems showed economic viability, with comparable revenues of USD 48/MT. SS-AD had a higher capital cost (USD 256/MT) but lower non-facility-dependent operating costs (USD 11/MT) than composting. Digestate drying was crucial for SS-AD’s profitability, despite being energy-intensive. Sensitivity analysis highlighted the importance of plant size, tipping fees, and by-product prices. SS-AD is more suitable for centralized management, while composting suits decentralized management of yard trimmings. Additionally, the simulation of the entire process flow allows the estimation of energy production, cost analysis, and material balances, that constitutes key data for determining Renewable Identification Numbers (RINs) and understanding compliance with the Renewable Fuel Standard (RFS2) [54].

The production of bioethanol has been studied in several cases with SuperPro Designer by using several raw materials: the most important lignocellulosic residues. However, some authors carried out research using other sources, for instance Ferrari et al. [63] demonstrated that the use of sweet potato for bioethanol production is viable with certain optimal conditions to minimize energy consumption. High dry matter concentration, improved fermentation efficiency, and high sugar content are essential for economic feasibility using sweet potato. The drying process incurs substantial energy costs, suggesting that fresh sweet potato is more favorable for bioethanol production.

Regarding the use of lignocellulosic residues, the work of Kumar et al. [71] is remarkable in its use of cellulosic feedstocks like tall frescue grass to produce bioethanol. In this work it was found that the capital costs varied among different pretreatment technologies, with the steam explosion process having the lowest capital cost. Ethanol production costs ranged from USD 0.81 to USD 0.89 per liter, with hot water pretreatment being the most cost-effective method. The sensitivity analysis highlighted that the price of biomass and the efficiency of pentose fermentation significantly impacted the overall cost of ethanol production. It was also found that energy requirements and capital costs were interdependent, with adjustments needed in the distribution of process water to optimize costs and energy use. Also, the production of bioethanol from wheat straw was studied by Hasanly et al., [72] resulting in a potentially feasible and economical method. Among the main results, it was found that a base plant size of 316 tons per day can produce about 20 million liters of ethanol annually. Increasing the plant size can significantly reduce the fixed capital investment per unit of capacity. The minimum selling price of ethanol can be reduced by increasing the fraction of collectable residue, improving wheat yields, and increasing the fraction of available farmland for collecting wheat straw. Sensitivity analysis showed that the operating cost is highly sensitive to the costs of wheat straw, cellulase enzyme, high-pressure steam, chilled water, and organic waste treatment. The risk assessment using Monte Carlo simulation indicated that the project is fairly low-risk at moderate to high ethanol selling prices.

The production of biochar from lignocellulosic residues is also been an important process lately. Several authors have examined the economic and sustainability implications of converting solid residues into biochar. For instance, Giwa et al., [62] studied the process of converting date palm waste to biochar using concentrated solar thermal energy. This study highlighted the economic and environmental sustainability implications of this process by benchmarking it against conventional electric heating-based pyrolysis. The process involves stages such as feedstock preparation, pyrolysis, heat recovery, and biochar collection. Sensitivity analysis explored variables like plant size, feedstock cost, and biochar yield. Key findings reveal that biochar production is economically viable with the right market conditions, presenting a sustainable solution for managing agricultural waste. The findings indicated that the solar-based process was more economically viable with a payback time (PBT) of 4 years and 132 days, an internal rate of return (IRR) of 14.8%, a return on investment (ROI) of 22.9%, and a gross margin of 35.5%. Furthermore, the environmental impact assessment revealed that CO₂ emissions from the solar energy-based pyrolysis accounted for only 38% of those from the conventional process, demonstrating its environmental friendliness. The study concluded that the solar concentration technique is economically viable, environmentally friendly, and presents a sustainable opportunity for biochar production while reducing life-cycle emissions and costs.

4.2. Challenges of Using SuperPro Designer for Large-Scale Studies

Conducting techno-economic analysis in SuperPro Designer for large-scale bioprocess demands precise experimental data to be used during the configuration of the operation units; however, it is important to note that the simulation of a large-scale bioprocess cannot be fully representative of the reality, as real operational challenges are not considered during simulations [73]. Also, most of the information used during the simulations was retrieved from experiments performed at laboratory scale, which lacks scalability studies and the same occurred for the downstream processing. Translating laboratory-scale data to industrial-scale operations involves numerous scale-up challenges including changes in mixing, heat transfer, and reaction kinetics, as stated before.

Regarding the use of lignocellulosic biomass as raw material, this presents several significant challenges. Firstly, the complexity of pretreatment processes is notable since lignocellulosic biomass requires extensive pretreatment to break down the complex structure of cellulose, hemicellulose, and lignin [74]. Accurately modeling these steps in SuperPro Designer can be challenging due to the need for detailed kinetic and thermodynamic data [57]. Additionally, lignocellulosic waste often exhibits significant variability in composition and quality, impacting on the reliability and accuracy of the simulation and making it difficult to standardize processes and predict performance consistently. Large-scale bioprocesses typically involve multiple sequential and parallel operations such as hydrolysis, fermentation, product recovery, and purification. Integrating these steps into a cohesive and accurate model in SuperPro Designer can be complex and time-consuming [73,75,76]. Lastly, assessing the environmental impact of the processes, including waste generation, emissions, and resource consumption, is increasingly important. Although. SuperPro Designer does not provide tools for comprehensive environmental impact assessments, it provides a comprehensive mass and energy balances for inputs and outputs that can help to support sustainable process design by using this information with other software packages [66,73,75,77]. Finally, it is important to note that the type of bioproduct, as well as the scale of the process, will determine not only the bioprocess design but also the economic values for several indicators. For instance, a monoclonal antibody is an expensive bioproduct, and the production of only 11 kg per year will demand high CAPEX; however, the determined ROI value is clearly superior to other bioproducts listed in

Table 2. Economic evaluation of some industrial processes simulated in SuperPro Designer.

Reference	Product	CAPEX (Million USD)	OPEX (Million USD)	Payback Time (Years)	NPV (Million USD)	ROI (%)	GM (%)
[52]	11 kg/year of Monoclonal antibodies	48.4	92.9	ND	ND	115	47
[59]	695.6 kg/year of carotenoids and microbial oil	2.268	0.82	2.44	4.235	40.95	59.65
[69]	1.7 MT/day of crystallized caffeine and powdered catechins	60.7	44.616	6.79	4.379	14.74	12.28
[70]	510 MT/year of malic acid	7.067	1.179	7.78	2.246	12.85	45.16
[78]	3.95 million gallons of biodiesel	534.07	149	ND	ND	ND	ND

. On the other hand, a plant for malic acid production, which is significantly larger, has lower CAPEX and ROI values given the nature of the biomolecule and its derived applications.

5. Conclusions

Process design methodologies for accurate decision-making on transformation projects of required active compounds in societies have evolved with chemical engineering, biotechnological engineering, numerical methods, and computational sciences. Systemic and systematic process design is nowadays approached through specialized software for process or even bioprocess design. In process simulation as a technical methodology, we converge knowledge of chemical and biochemical unitary processes, physical properties of substances, mathematical treatment for mass and energy balances, cost estimation, and environmental impact, among other important theoretical concepts and experimental data, with the aim of reaching the best possible anticipated knowledge on process performance and feasibility regarding economic and environmental criteria. This line of thought directs us to make the best decisions regarding the production of goods and services that human societies need. Moreover, a better understanding of production/consumption chains leads to a circular economy, rather than a high resource-demanding and waste-producing linear economy. The circular economy searches for value recovering from waste processing in general. Thus, new processes taking waste as inputs or raw materials for obtaining bioenergy compounds or any other valued substances must be designed with the more advanced methods and technical resources available, which are thorough biochemical fundamentals of the process, solid experimental results from the laboratory, highly instrumentalized bench or pilot level equipment for accurate measurements, extensive economic data, clear and sufficient environmental regulations for ecosystems protection and advanced process simulators such as SuperPro Designer.

An increasing number of projects within the CE concept have been studied recently. This trend would produce a robust selection of the best processes that will deliver valued products by using waste materials and then reducing pollution problems derived from other transformation processes. In this way, the linear economy would eventually transform into a circular economy for saving greenhouse gas emissions, waste materials, and energetic resources in the processing of goods and services. Process simulations that incorporate techno-economic analysis would be crucial in developing industries that utilize waste as raw material, thereby transforming waste into valuable products and reducing environmental impact.

Finally, it is important to note that in process engineering, significant advancements are expected soon, particularly in two main areas: bioprocess technology and software applications. Research will drive the development of new bioprocesses, process intensification, controlled micro-processes, and the use of microfluidic devices. Meanwhile, software improvements will include integrating economic databases, environmental criteria, and artificial intelligence techniques.