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A significant amount of municipal solid waste (MSW) generation and ineffective management can cause adverse impacts on the environment (air and water pollution) and human health. Therefore, a sustainable approach for handling MSW is required. This study aims to develop a tool to select the most promising MSW management system using the superstructure optimization technique to ensure that the selected system can achieve minimization of the total annual cost and environmental impacts. This can lead to a multi-objective superstructure optimization problem solved by using the augmented ϵ -constraint method. The developed model is based on the concept of material balance and all potential technologies include anaerobic digestion, pyrolysis, gasification, incineration, sanitary landfill, material recovery facility and composting. The proposed methodology is applied to synthesize the MSW management system in Warin Chamrap district to determine its applicability. In this research, the composition of MSW includes organics, paper, plastic, glass, and others. The results show that the Pareto-optimal solutions optimizing both total annual cost and environmental impacts can be developed. The MSW management system with the lower total annual cost tends to have the higher environmental impacts and the trade-offs between each objective can be identified so the decision-makers are able to consider before making their decision. Further investigation on sensitivity analysis reveals that the selling prices of products, annual capital cost, and operating cost factors have different degree of impacts on the waste management strategies and total annual cost. [ABSTRACT FROM AUTHOR]

The rising demand for greenhouse cultivation poses a challenge in providing environmentally friendly energy to maintain favorable greenhouse indoor conditions. This research presents an optimization model to identify cost-optimal symbiotic pathways to replace the import of three key greenhouse crops (tomato, cucumber, lettuce) with local greenhouse production. The proposed solutions involve greenhouses that can meet these crops' heat and CO2 demands via industrial symbiosis. Switzerland is investigated as the case study, and the (waste) heat suppliers are municipal solid waste incinerators, cement production plants and biogas plants. Upgrading biomethane production plants are also considered potential CO2 suppliers. The objective is to minimize the discounted cost when replacing 25%–100 % of vegetable imports with local agricultural greenhouses, considering availability of suitable land as well as waste heat and CO2 suppliers. Optimization results suggest prioritizing northeast and west Switzerland for greenhouse development, due to the availability of suitable land and proximity of waste heat and CO2 suppliers. Waste incinerators could provide 50%–70 % of the necessary heat, while Organic Rankine cycle in cement plants could generate 63 % of the electricity of supplementary lighting demand of greenhouses. While tailored for Switzerland, the optimization framework's general formulation can be adapted also to other regions to optimize greenhouses' heat and CO2 demands. The optimization code is provided open source for associated applications.

This paper introduces a simultaneous optimization approach to synthesizing work and heat exchange networks (WHENs). The proposed work and heat integration (WHI) superstructure enables different thermodynamic paths of pressure and temperature-changing streams. The superstructure is connected to a heat exchanger network (HEN) superstructure, enabling the heat integration of hot and cold streams identified within the WHI superstructure. A two-step solution strategy is proposed, consisting of initialization and design steps. In the first step, a thermodynamic path model based on the WHI superstructure is combined with a model for simultaneous optimization and heat integration. This nonlinear programming (NLP) model aims to minimize operating expenditures and provide an initial solution for the second optimization step. In addition, hot and cold streams are identified, enabling additional model reduction. In the second step of the proposed solution approach, a thermodynamic path model is combined with the modified HEN model to minimize the network's total annualized cost (TAC). The proposed mixed integer nonlinear programming (MINLP) model is validated by several examples, exploring the impact of the equipment costing and annualization factor on the optimal network design. The results from these case studies clearly indicate that the new synthesis approach proposed in this paper produces solutions that are consistently similar to or better than the designs presented in the literature using other methodologies. [ABSTRACT FROM AUTHOR]

As the consumption of electronics increases worldwide, significant strain is posed on both the availability of mineral resources and the accumulation of waste due to their disposal. Recovering valuable minerals from e-waste can potentially alleviate both. This paper discusses the systematic design of processes for the recycling of waste printed circuit boards (WPCB). After reviewing the relevant processing steps, the generation of processing superstructures is explained. Next, a formulation of the optimization problem is presented to identify the best processing pathway, and the use of process simulators to specify optimization-relevant parameters. These ideas are described in detail via a WPCB to metals case study.

Spain, in the framework of the European Green Deal, has set ambitious climate and energy goals for 2030. Lignocellulosic biorefineries can make a strong contribution to these goals; however, advanced biofuels generally cannot be produced in a profitable way compared to fossil and first-generation fuels. To address this problem, the efficient production of a wide range of bioproducts, not only biofuels and bioenergy, may be the solution. Therefore, a systematic evaluation of the near-term economic potential bio-building blocks (xylitol, sorbitol, succinic, glutamic, glucaric, levulinic, lactic, and itaconic acids) is presented. Many possible combinations of feedstock and conversion technologies can be considered feasible pathways to manufacture advanced biofuels and bio-based building blocks. To map the optimal groups of technologies in the framework of Spain's biorefineries, we apply a methodology based on a network optimization approach that combines minimum cost and energy criteria together with feedstock availability and demand constraints. The feedstocks analyzed are pine and eucalyptus residues and olive tree pruning wastes, being three largely available agroforesty residues in Spain. The results show that building blocks show good economic and energy performance compared to advanced transportation biofuels, and although their demand is much lower, they should be considered to improve the profitability of biorefineries. Secondly, advanced gasoline, bioethanol, hydrogen, and building blocks demands can be satisfied with pine, eucalyptus, and olive residues available in Spain. Finally, lactic acid production is profitable, but the remaining routes do not reach the break-even point, suggesting that further research is still needed. [ABSTRACT FROM AUTHOR]

This paper introduces a simultaneous optimization approach to synthesizing work and heat exchange networks (WHENs). The proposed work and heat integration (WHI) superstructure enables different thermodynamic paths of pressure and temperature-changing streams. The superstructure is connected to a heat exchanger network (HEN) superstructure, enabling the heat integration of hot and cold streams identified within the WHI superstructure. A two-step solution strategy is proposed, consisting of initialization and design steps. In the first step, a thermodynamic path model based on the WHI superstructure is combined with a model for simultaneous optimization and heat integration. This nonlinear programming (NLP) model aims to minimize operating expenditures and provide an initial solution for the second optimization step. In addition, hot and cold streams are identified, enabling additional model reduction. In the second step of the proposed solution approach, a thermodynamic path model is combined with the modified HEN model to minimize the network’s total annualized cost (TAC). The proposed mixed integer nonlinear programming (MINLP) model is validated by several examples, exploring the impact of the equipment costing and annualization factor on the optimal network design. The results from these case studies clearly indicate that the new synthesis approach proposed in this paper produces solutions that are consistently similar to or better than the designs presented in the literature using other methodologies.

This article addresses the sustainable design of hydrogen (H2) production systems that integrate brown and blue pathways with green hydrogen infrastructure. We develop a systematic framework to simultaneously optimize the process superstructure and operating conditions of steam methane reforming (SMR)‐based hydrogen production systems. A comprehensive superstructure that integrates SMR with multiple carbon dioxide capture technologies, electrolyzers, fuel cells, and working fluids in the organic rankine cycle is proposed under varying operating conditions. A life cycle optimization model is then developed by integrating superstructure optimization, life cycle assessment approach, techno‐economic assessment, and process optimization using extensive process simulation models and formulated as a mixed‐integer nonlinear program. We find that the optimal unit‐levelized cost of hydrogen ranges from $1.49 to $3.18 per kg H2. Moreover, the most environmentally friendly process attains net‐zero life cycle greenhouse gas emissions compared to 10.55 kg CO2‐eq per kg H2 for the most economically competitive process design. [ABSTRACT FROM AUTHOR]

Microalgae have attracted great research interest as a feedstock for producing a wide range of end-products. However, recent studies show that the tight processing integration technology for microalgae-based biorefinery makes production less economical and even has a negative impact on sustainability. In this study, a new two-tier superstructure optimization design methodology is proposed to locate the optimal processing pathway. This model is developed based on the decomposition strategy and the relationship-based investigation, coupling an outer-tier structure with an inner-tier structure, wherein the outlet flows of the middle stages is relaxed and then an appropriate level of redundancy for designing the processing is provided. Two scenarios are developed to compare the most promising biorefinery configurations under two different design option favors. By solving the mixed integer nonlinear programming model with the objective functions of maximizing the yield of the desired products and maximizing the gross operating margin, the optimization results obtained show the ability of this framework to provide the promising configurations and cost-effectiveness of microalgae-based biorefinery. Compared with Scenario 1, the optimized solutions in Scenario 2 feature a gross operating margin increase up to 27.09% and an increase in product yield up to 25.00%. The proposed method improves the original huge computing scale and ensures economics without simplifying the processing pathways. [ABSTRACT FROM AUTHOR]

A shift from fossil fuels to renewable energy sources is essential to reduce global greenhouse gas emissions and climate change effects. Biofuels represent a promising low-carbon alternative for sectors that are hard to electrify, such as freight transport or aviation. This work investigates possible pathways for increasing the value of biomass at a Kraft pulp mill, focusing on black liquor and bark streams. Mathematical programming is coupled with superstructure optimization and systematic solution exploration to identify meaningful process configurations. The analysis of solutions under market variations allows for the identification of robust and competitive configurations for the co-production of pulp and fossil fuel alternatives. The results show that the integration of biorefineries in pulp mills results in better resource use and higher energy efficiency - diversifying the product portfolio and providing bio-based fuel products to the market while being economically viable. By incorporating fuel production in the conventional Kraft process, the carbon conversion efficiency of the mill can be increased from 48% to up to 67%. Extending the analysis, up to 2% of the European road freight transportation fuel could be provided with combined pulp and fuel production, and 5% of the worldwide fuel demand for passenger aviation.

This paper addresses the optimization of geothermal energy-powered seawater desalination systems. A nonlinear optimization mathematical model was implemented into General Algebraic Modelling System (GAMS). The model embeds many candidate configurations for optimization, which result from the combination of the following subsystems: a single flash (SFGP) or double flash (DFGP) geothermal power plants, a multi-effect distillation (MED) process or a reverse osmosis (RO) system for freshwater production. Also, an energy recovery turbine (ERT) is embedded. The minimization of the total annual cost of the process represents the objective function. The proposed model is a powerful decision-making tool for selecting the optimal configuration of the integrated process, sizes, and operating conditions to meet the demands of electricity and freshwater desired by users. It was found that for fixed 70 MW electricity generation and low freshwater demands (720–2880 m3/h), the optimal configuration is SFGP/MED. For high freshwater demands (4320–5040 m3/h), the optimal configuration is DFGP/MED/RO. For intermediate freshwater demand (3600 m3/h), an optimal transition configuration is in between the two previous configurations, i.e., SFGP/MED/RO/ERT. It is highlighted that the model can be used to simulate fixed configurations if no degrees of freedom are available. [ABSTRACT FROM AUTHOR]

The rising demand for greenhouse cultivation poses a challenge in providing environmentally friendly energy to maintain favorable greenhouse indoor conditions. This research presents an optimization model to identify cost-optimal symbiotic pathways to replace the import of three key greenhouse crops (tomato, cucumber, lettuce) with local greenhouse production. The proposed solutions involve greenhouses that can meet these crops' heat and CO 2 demands via industrial symbiosis. Switzerland is investigated as the case study, and the (waste) heat suppliers are municipal solid waste incinerators, cement production plants and biogas plants. Upgrading biomethane production plants are also considered potential CO 2 suppliers. The objective is to minimize the discounted cost when replacing 25%–100 % of vegetable imports with local agricultural greenhouses, considering availability of suitable land as well as waste heat and CO 2 suppliers. Optimization results suggest prioritizing northeast and west Switzerland for greenhouse development, due to the availability of suitable land and proximity of waste heat and CO 2 suppliers. Waste incinerators could provide 50%–70 % of the necessary heat, while Organic Rankine cycle in cement plants could generate 63 % of the electricity of supplementary lighting demand of greenhouses. While tailored for Switzerland, the optimization framework's general formulation can be adapted also to other regions to optimize greenhouses' heat and CO 2 demands. The optimization code is provided open source for associated applications. • A super structure optimization framework for sustainable greenhouses. • Industrial symbiosis opportunities for heat and CO 2 network. • The technology impact in conducting an efficient symbiotic exchange. • The effect of co-location on the potential of waste heat recovery. • The contribution of ORC for compensation of the supplementary lighting demand. [ABSTRACT FROM AUTHOR]

This study introduces an innovative methodology for early-stage biorefinery design and analysis through the integration of metabolic models into superstructure optimization. The focus is on two types of metabolic models: i) those where ideal growth conditions are assumed, and ii) those where growth is dependent on environmental variables. Metabolic models offer a comprehensive and dynamic representation of the bioreactor system, unbound by the limitations of traditional kinetic models or the need of extensive experimental data. The integration of these models involves a curation and validation process, ensuring that the metabolic capabilities of the microorganisms are accurately represented. Once validated, the models are plugged into the superstructure in the form of surrogate equations that can be handled by the optimization problem for the superstructure. The effectiveness of this methodology is then tested and critically analyzed with the help of two case studies. The primary contribution of this work lies in its effective identification of the most favorable combinations of substrates and microorganisms. The outcome establishes the feasibility of utilizing different substrates and microorganisms in a biorefinery context, thereby highlighting the value of metabolic models in a superstructure optimization framework for exploring early-stage biorefinery schemes. This approach holds significant promise in enhancing the efficiency and sustainability of future biorefinery systems. • Metabolic models incorporated in biorefinery superstructure optimization. • Metabolic models to link multiple substrates to many microorganisms. • Genome scale models representing pure cultures. • Consortia models representing mixed culture fermentations. • Economically viable early-stage biorefinery design. [ABSTRACT FROM AUTHOR]

This work presents a superstructure optimization study for the production of renewable fuels with a focus on jet fuel. Power-to-X via the methanol (MTJ) and Fischer–Tropsch (FT) route is combined with Biomass-to-X (BtX) via an algae-based biorefinery to an integrated Power- and Biomass-to-X (PBtX) process. Possible integration by algae remnant utilization for H2/CO2 production, wastewater recycling and heat integration is included. Modeling is performed using the novel Open sUperstrucTure moDeling and OptimizatiOn fRamework (OUTDOOR). Novel methods to account for advanced mass balances and uncertain input data are included. Economic optimization proposes a PBtX process. This process combines algae processing with MTJ and depicts a highly mass- and energy integrated plant. It produces fuels at 211 EUR/MWhLHV (ca. 2530 EUR/t), a cost reduction of 21% to 11.5% compared to stand-alone electricity- or bio-based production at algae costs of 25 EUR/tAlgae-sludge and electricity costs of 72 EUR/MWh. Investigation of uncertain data indicates that a combination of BtX and MTJ is economically superior to FT for a wide parameter range. Only for high algae costs of >40 EUR/tAlgae-sludge stand-alone electricity-based MTJ is economically superior and for high MTJ costs above 2000–2400 EUR/tJet FT is the optimal option. [ABSTRACT FROM AUTHOR]

In Malaysia, palm oil industries have played significant roles in the economic sectors and the nation's developments. One aspect of these industries that is gaining growing interest is oil palm residue management and bio-based product generations. EFB has been identified to be a feasible raw material for the production of bio-energy, bio-chemicals, and bio-materials. In this paper, our previous deterministic mathematical programming model was extended to include decisions for selecting optimal transportation modes and processes at each level of the processing stage in the supply chain. The superstructure of alternatives was extended to show states of produced products whether solid, liquid, or gaseous, and for which truck, train, barge, or pipeline would be possible modes of transportation. The objective function was to maximize profit which accounts for associated costs including the emission treatment costs from production and transportation. The optimal profit was USD 1,561,106,613 per year for single ownership of all facilities in the supply chain. [ABSTRACT FROM AUTHOR]

It is crucial to leave behind the traditional linear economy approach. Shifting the paradigm and adopting a circular (bio)economy seems to be the strategy to decouple economic growth from continuous resource extraction. To this end, producing bio-based products that aim to replace a part, if not all, of the fossil-based chemicals and fuels is a promising step. This can be achieved by using multi-product integrated biorefineries that convert organic wastes into chemicals, fuels, and bioenergy to optimize the use and close the materials and energy loops. To further address the development and implementation of organic waste integrated biorefineries, we proposed the open-source organic waste to value-added products (O2V) model and multi-objective optimization tool. O2V aims to provide a quick and straightforward holistic assessment, leading to identifying optimal or near-optimal design, planning, and operational decisions. This model not only prioritizes economic benefits but also takes on board the other pillars of sustainability. The proposed tool is built on a comprehensive superstructure of processing alternatives that include all stages concerning the conversion of organic waste to value-added products. Furthermore, it has been framed and formulated in a “plug-and-play” format, where, when required, the user only needs to add new process data to the structured information database. This database integrates data on (i) new processes (e.g., different conversion technologies), (ii) feedstocks (e.g., composition), and (iii) products (e.g., prices), among others. Due to Denmark’s high availability of organic waste, implementing a second-generation integrated biorefinery in Denmark has been chosen as a realistic showcase. The application of O2V efficiently led to the identification of trade-offs between the different sustainability angles. Thus, it made it possible to determine early-stage decisions regarding product portfolio, optimal production process, and related planning and operational decisions. Henceforth, it has been demonstrated that applying O2V aids in shifting the fossil to bio-based production, thereby contributing to the switch toward a circular bioeconomy.

The conceptual process design of novel bioprocesses in biorefinery setups is an important task, which remains yet challenging due to several limitations. We propose a novel framework incorporating superstructure optimization and simulation-based optimization synergistically. In this context, several approaches for superstructure optimization based on different surrogate models can be deployed. By means of a case study, the framework is introduced and validated, and the different superstructure optimization approaches are benchmarked. The results indicate that even though surrogate-based optimization approaches alleviate the underlying computational issues, there remains a potential issue regarding their validation. The development of appropriate surrogate models, comprising the selection of surrogate type, sampling type, and size for training and cross-validation sets, are essential factors. Regarding this aspect, satisfactory validation metrics do not ensure a successful outcome from its embedded use in an optimization problem. Furthermore, the framework's synergistic effects by sequentially performing superstructure optimization to determine candidate process topologies and simulation-based optimization to consolidate the process design under uncertainty offer an alternative and promising approach. These findings invite for a critical assessment of surrogate-based optimization approaches and point out the necessity of benchmarking to ensure consistency and quality of optimized solutions. [ABSTRACT FROM AUTHOR]

Abstract The main objective of this study is to develop a decision-making tool for the design of the optimal municipal solid waste (MSW) facilities based on superstructure optimization. Currently, the disposal of MSW is a major problem due to the lack of awareness of the negative impacts resulting from dumping MSW into the environment. This poses a challenge for the authorities. MSW valorization such as anaerobic digestion, pyrolysis, and gasification has been increasingly focused on as an approach when handling MSW to enhance both economic and environmental sustainability. However, with an increasing array of processing technologies, the design of MSW facilities involving the integration of these technologies is becoming tedious and unmanageable. To deal with this problem, superstructure optimization is proposed. It is an effective tool for the design of several chemical processes because it is able to consider all potential process alternatives including the optimal solution using mathematical models based on mass and energy balances. Uncertainty is incorporated into the optimization framework to enhance the robustness of the solution. The proposed methodology was applied in the design process of the MSW facility in Ubon Rathathani Province, Thailand, with the objective function of maximizing the profit. The optimization problem was developed as Mixed Integer Linear Programming and it was solved using an optimization platform, General Algebraic Modeling System, with CPLEX as the solver related to obtaining the optimal solution. The results show there to be as positive profit that is economically viable compared to the use of landfill technology.

This manuscript describes the conceptual process design of an integrated xylitol biorefinery with value-added co-products. Based on an existing three-step framework, the main product of a second-generation integrated biorefinery is chosen in the first stage. Based upon this, other decisions as the feedstock and value-added co-products are made. All relevant unit operations for the process are introduced. An initial superstructure with all potential process alternatives is composed of all introduced models. In the second step of the framework, a global sensitivity analysis is performed, firstly with coarse sampling to determine all viable flowsheet options and secondly with fine sampling to determine the most sensitive operational variables. As a result of the sensitivity analysis, most of the flowsheet options in the initial superstructure are not feasible. Based on these results, flowsheet sampling with the five most sensitive operational variables is performed to create surrogate models. In the scope of this work, three types of surrogate models are benchmarked against each other. Regarding the results of the superstructure optimization, firstly, it becomes apparent that the production of biokerosene does not contribute significantly to the net present value of the biorefinery. Furthermore, reducing the number of unit operations in the downstream processing leads to lower capital expenditures, but it lowers the product yield. Lastly, most flowsheets are economically feasible, indicated by a positive net present value. Based on this result, the most promising candidate process topology is subjected to the third step of the framework, including uncertainty in capital expenditure and operational expenses according to their estimations and uncertainties in the product prices. As a result, the net present value of the flowsheet turns negative, indicating that the high uncertainties for the expenditure and the expenses do not allow for an economically feasible operation. Lastly, the analysis of conceptually designed process flowsheets based on Monte Carlo sampling shows failure rates, with the NPV falling below the break-even point, of around 60% probability or higher. Based on these results, an economically feasible construction and operation of a xylitol biorefinery seems unlikely. Further ways to improve the metrics are elucidated.

[Display omitted] • A methodology for process synthesis by superstructure optimization is proposed. • A multiscale approach is considered for modelling the ammonia synthesis loop. • Kinetic rates of Fe- and Ru-based catalysts are evaluated. • The number of compressors, of reactors, and the type of cooling is variable. • The optimal configuration requires an energy consumption of 10.67 kW h/kg NH 3. Ammonia has a potential as carbon-free and high-energy density compound for chemical storage of renewable energies and its synthesis from green H 2 requires to be as energetically efficient as possible. In this work, a superstructure optimization methodology for process synthesis is proposed and applied to an ammonia production process. The approach covers three different scales: process, equipment, and molecules. Process scale refers to finding the optimal process structure, while the equipment scale is related to the best set of operating conditions, and the molecular scale studies two catalysts (Fe and Ru) with their respective kinetic rates. The optimization intends to minimize the Levelized Cost of Ammonia (LCOA) and maximize the energy efficiency. The best trade-off is found with the Ru catalyst, with an LCOA of 766 €/tNH 3 and 57.2% of energy efficiency. In comparison with reference cases, the LCOA decreases 0.6% and the energy efficiency increases 1.5%. The main improvement is found in the pressure, 75 bar, reduced in 25%. The production of 11.6 tNH 3 /day equals to 2.5 MW of stored power from 3 MW supplied as H 2 to the process, with a total energy consumption of 10.67 kW h/kgNH 3 , including prior H 2 and N 2 production processes. [ABSTRACT FROM AUTHOR]

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm for replacing non-linear process simulation models integrated in multi-level optimization of a process and energy system superstructure with surrogate models, applying an active learning strategy to continuously enrich the database on which the surrogate models are trained and evaluated. Surrogate models are generated and trained on an initial data set, each featuring the ability to quantify the uncertainty with which a prediction is made. Until a defined prediction quality is met, new data points are continuously labeled and added to the training set. They are selected from a pool of unlabeled data points based on the predicted uncertainty, ensuring a rapid improvement of surrogate quality. When applied in the optimization superstructure, the surrogates can only be used when the prediction quality for the given data point reaches a specified threshold, otherwise the original simulation model is called for evaluating the process performance and the newly obtained data points are used to improve the surrogates. The method is tested on three simulation models, ranging in size and complexity. The proposed approach yields mean squared errors of the test prediction below 2% for all cases. Applying the active learning approach leads to better predictions compared to random sampling for the same size of database. When integrated in the optimization framework, simpler surrogates are favored in over 60% of cases, while the more complex ones are enabled by using simulation results generated during optimization for improving the surrogates after the initial generation. Significant time savings are recorded when using complex process simulations, though the advantage gained for simpler processes is marginal. Overall, we show that the proposed method saves time and adds flexibility to complex superstructure optimization problems that involve optimizing process operating conditions. Computational time can be greatly reduced without penalizing result quality, while the continuous improvement of surrogates when simulation is used in the optimization leads to a natural refinement of the model.

Microalgae have attracted great research interest as a feedstock for producing a wide range of end-products. However, recent studies show that the tight processing integration technology for microalgae-based biorefinery makes production less economical and even has a negative impact on sustainability. In this study, a new two-tier superstructure optimization design methodology is proposed to locate the optimal processing pathway. This model is developed based on the decomposition strategy and the relationship-based investigation, coupling an outer-tier structure with an inner-tier structure, wherein the outlet flows of the middle stages is relaxed and then an appropriate level of redundancy for designing the processing is provided. Two scenarios are developed to compare the most promising biorefinery configurations under two different design option favors. By solving the mixed integer nonlinear programming model with the objective functions of maximizing the yield of the desired products and maximizing the gross operating margin, the optimization results obtained show the ability of this framework to provide the promising configurations and cost-effectiveness of microalgae-based biorefinery. Compared with Scenario 1, the optimized solutions in Scenario 2 feature a gross operating margin increase up to 27.09% and an increase in product yield up to 25.00%. The proposed method improves the original huge computing scale and ensures economics without simplifying the processing pathways.

In this work, we present a systematic approach for solvent selection and process intensification synthesis based on the Generalized Modular Representation Framework (GMF). GMF is a bottom‐up process synthesis strategy which leverages mass and/or heat transfer building blocks to represent chemical equipment and flowsheets. Thus, conventional and/or intensified process solutions can be generated to obtain optimal process design(s) without a pre‐postulation of equipment or flowsheet configurations. To address the challenge of module dimensionality estimation for capital cost correlation, orthogonal collocation is coupled to enhance GMF intra‐module representation. Material selection is achieved by incorporating physical property models (e.g., NRTL) in GMF model formulation to describe the nonideal liquid mixture behaviors and to assess solvent performance in facilitating separation. In this context, solvent selection and process intensification synthesis are simultaneously addressed within a superstructure‐based optimization formulation. The proposed approach is demonstrated on two case studies for ethanol‐water extractive separation, with two sets of solvents evaluated: (a) ethylene glycol versus methanol to obtain 99 mol% ethanol, and (b) ethylene glycol versus 1‐ethyl‐3‐methyl‐imidazolium acetate ([EMIM][OAc]) to obtain 99.8 mol% ethanol and 99 mol% water. [ABSTRACT FROM AUTHOR]

When using superstructure modeling method to optimize the waste hydrogen stream recovery network, it is often difficult to solve the model, or even unable to get the optimal solution. In this paper, the components in the waste hydrogen stream are characterized as hydrogen, condensable light hydrocarbons and fuel gas. The triangular coordinate characterization is established, and the resource recovery optimization method is discussed, so as to form the corresponding constraints on the material balance and stream flow direction. Combined with mathematical constructions, an improved superstructure optimization model is established. The model is verified with a case from a refinery. The results show that under the same calculation conditions, the conventional modeling method and the improved modeling method obtain calculation results after 5.3 seconds and 1.6 seconds respectively. The former does not achieve the optimal solution for some streams, only obtains the suboptimal solution, while the improved method is faster and obtains the best matching network. The annual economic benefits of the two are 10.63 million yuan and 21.44 million yuan respectively, and the latter gains 10.81 million yuan per year more than the former. [ABSTRACT FROM AUTHOR]

In Malaysia, palm oil industries have played significant roles in the economic sectors and the nation’s developments. One aspect of these industries that is gaining growing interest is oil palm residue management and bio-based product generations. EFB has been identified to be a feasible raw material for the production of bio-energy, bio-chemicals, and bio-materials. In this paper, our previous deterministic mathematical programming model was extended to include decisions for selecting optimal transportation modes and processes at each level of the processing stage in the supply chain. The superstructure of alternatives was extended to show states of produced products whether solid, liquid, or gaseous, and for which truck, train, barge, or pipeline would be possible modes of transportation. The objective function was to maximize profit which accounts for associated costs including the emission treatment costs from production and transportation. The optimal profit was USD 1,561,106,613 per year for single ownership of all facilities in the supply chain.

This article addresses the sustainable design and synthesis of open‐loop recycling process of waste high‐density polyethylene (HDPE) under both environmental and economic criteria. We develop by far the most comprehensive superstructure for producing monomers, aromatic mixtures, and fuels from waste HDPE. The superstructure optimization problem is then formulated as a multi‐objective mixed‐integer nonlinear fractional programming (MINFP) problem to simultaneously optimize the unit net present value (NPV) and unit life cycle environmental impacts. A tailored global optimization algorithm integrating the inexact parametric algorithm with the branch‐and‐refine algorithm is applied to efficiently solve the resulting nonconvex MINFP problem. Results show that the optimal unit NPV ranges from $107.2 to $151.3 per ton of HDPE treated. Moreover, the unit life cycle greenhouse gas emissions of the most environmentally friendly HDPE recycling process are 0.40 ton CO2‐eq per ton of HDPE treated, which is 63% of that of the most economically competitive process design. [ABSTRACT FROM AUTHOR]

We propose a general shortcut distillation column model that can address a wide range of types of separations including nonsharp/sloppy splits. The model can automatically identify key components and estimate the energy requirement of a desired separation in the presence, potentially, of components with zero flow rates. To obtain more practical column height and vapor flow rates, we augment the model with constraints inspired by the Fenske equation. We also propose valid constraints to enhance the computational performance of the proposed model. Due to its flexibility and computational efficiency, the proposed model can be readily used for superstructure‐based process synthesis. [ABSTRACT FROM AUTHOR]

The key to many chemical and energy conversion processes is the choice of the right molecule, for example, used as working fluid. However, the choice of the molecule is inherently coupled to the choice of the right process flowsheet. In this work, we integrate superstructure‐based flowsheet design into the design of processes and molecules. The thermodynamic properties of the molecule are modeled by the PC‐SAFT equation of state. Computer‐aided molecular design enables considering the molecular structure as degree of freedom in the process optimization. To consider the process flowsheet as additional degree of freedom, a superstructure of the process is used. The method results in the optimal molecule, process, and flowsheet. We demonstrate the method for the design of an organic Rankine cycle considering flowsheet options for regeneration, reheating, and turbine bleeding. The presented method provides a user‐friendly tool to solve the integrated design problem of processes, molecules, and process flowsheets. [ABSTRACT FROM AUTHOR]

This article addresses the sustainable design of organic Rankine cycle-based geothermal binary power systems under economic and environmental criteria. A novel super-structure with multiple heat source temperatures, working fluids, and heat rejection systems is proposed. Based on the superstructure, a life cycle optimization model is formulated as a mixed-integer nonlinear fractional program (MINFP) to determine the optimal design. The nonconvex MINFP is efficiently solved by a tailored global optimization algorithm. Two case studies are considered to demonstrate the proposed modeling framework and solution algorithm. One case is based on a geothermal energy system located in California, and the other one is in New York (NY) State. The results show that the geothermal energy system in California is much more economically competitive than that in NY State. The difference in life cycle environmental impacts is less pronounced because the environmental impacts are less sensitive to geological conditions than the capital investments. [ABSTRACT FROM AUTHOR]

Abstract The closed-loop network design of a hybrid manufacturing and recovering system plays an important role on improving both the production efficiency and environmental quality, especially under a multi-product multi-stage scenario. Besides, with the growing awareness of environmental concerns by legislators, decision makers have been forced not only to enhance utilization of the returned products or components but also to control carbon emission of the whole hybrid system. To optimize the closed-loop logistics superstructure design, a multi-objective model integrating the manufacturing and recovering process in a multi-product multi-stage environment is proposed in this research. Both operations cost and environmental influence are regarded as two objective categories. In addition, the carbon emission regulation is considered in the model by integrating and characterizing the energy input and greenhouse output throughout the hybrid system. Finally, a numerical case originated from an electronic assembly plant is presented under different scenarios to illustrate the effectiveness of the proposed approach. A sensitivity analysis is given to compare the impacts of carbon emission and return rate on each sub-objective of the logistics and environmental issues. Highlights • A closed-loop network for a hybrid manufacturing and recovering system is designed. • The energy input and greenhouse output models are proposed and embedded. • A multi-objective multi-echelon model for carbon-efficient scheduling is proposed. • Impacts on interdependence of economic and environmental dimensions are analyzed. [ABSTRACT FROM AUTHOR]

Abstract In this work a decision making framework for the design of the flue gas treatment section of a power plant has been developed, including particle, NOx and SO 2 removal operations. It has been applied to a coal based thermal power plant in Spain to select the optimal technologies and its sequence. Surrogate models for the treatments have been developed. The problem corresponds to a mixed integer non-linear programming one including catalytic and non-catalytic NOx removal, allowing various allocations for the catalytic technology, electrostatic precipitation and wet or dry SO 2 removal. It is reformulated as a non-linear problem to evaluate bypass opportunities. The optimization suggests the use of electrostatic precipitation, followed by catalytic NOx removal and dry SO 2 removal. Next, a coal blending problem has also been solved for two objective functions. When only treatment costs are considered, the use of imported coal is recommended, but an increase of 4% in its price can change the decision into the use of national coal. If the energy within the coal is added to the objective function, crude tar coal is included in the blend and imported coal is used to maintain the emissions within limits. Limestone Forced Oxidation is the selected technology. Highlights • Heuristics and mathematical optimization are used to select gas treatment. • Ash, catalytic NOx and dry SO2 removal are the selected technologies in this order. • Only imported coal is suggested if only treatment is analyzed. • Crude tar coal is added to the blend when power produced is included in the objective. • Limestone Forced Oxidation is a more flexible technology to process coal blends. [ABSTRACT FROM AUTHOR]

This study aims to develop a mathematical formulation of superstructure multi-objective optimization to find optimal pathways of high-CO 2 natural gas and PV-hydrogen to chemicals and fuels integrated with CO 2 sequestration. A mixed-integer nonlinear programming optimization model is used to compete along 56 pathways in the superstructure optimization network. The model was developed with the general algebraic modeling system and computed with the standard branch and bound (SBB) solver to maximize annual net profit and minimize annual net CO 2 emissions. The result shows the selected products in 2020 are liquefied natural gas, methanol, dimethyl ether, formic acid, and acetic acid, with annual net profits and net CO 2 emissions of 38.17 million $/year and −8.47 million tons of CO 2 /year, respectively. In the projection analysis, the annual net profit growth results at a compounding annual growth rate of 7.85% per year between 2020 and 2060, while annual net CO 2 emissions decrease until −16.37 million tons of CO 2 /year in 2060. In 2030, blue acetic acid will be selected via all separation processes. For 2050, the synfuel via partial membrane separation is selected. The other products, formic acid, methanol, and acetic acid via membrane separation (full and partial), will always be selected between 2020 and 2060. Therefore, this could be a future strategy for monetizing high–CO 2 –content natural gas sources with low-carbon process development. [Display omitted] • The superstructure network of CO 2 rich natural gas to chemicals and fuels. • Multi-period multi-objective optimization (MOO) was performed. • Trade-offs of MOO are obtained from the Pareto frontier via decision-making. • Blue products have been selected since 2040. • The trend of net-CO 2 emissions are decreasing until 2060 in optimal pathways. [ABSTRACT FROM AUTHOR]

• A two-stage optimization model is proposed for the design of integrated microalgae biofuel system. • In the first stage the carbon capture, utilization and storage (CCUS) network are taken into account • The second-stage is dedicated to biorefinery superstructure optimization for microalgae biomass. • A robust optimization model is developed handle the inherent uncertainty of critical input parameters. In recent years, several strategies have been developed and adopted in a bid to manage the biofuel supply chain. In this paper, a two-stage optimization model is proposed for integrated microalgae biofuel supply chain network design and superstructure optimization problems. In the first stage, the design of the carbon capture, utilization, and storage (CCUS) network is taken into account. A robust mixed integer linear programming (RMILP) model is proposed to optimize the strategic CCUS decisions, including the simultaneous selection of emission sources, capture facilitates, Carbon Dioxide (CO 2) pipelines, intermediate transportation sites and storage sites, or microalgae cultivation sites. The second stage is dedicated to biorefinery superstructure optimization in order to determine the optimal/promising biorefinery configurations. The presented model is able to handle the inherent uncertainty of critical input parameters. Moreover, the results show that biodiesel production cost cannot compete with current diesel price, but it can be reduced significantly by improving biomass productivity. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs) have been used in a wide range of applications for complex datasets with their flexible mathematical architecture. The flexibility is favored by the introduction of a higher number of connections and variables, in general. However, over-parameterization of the ANN equations and the existence of redundant input variables usually result in poor test performance. This paper proposes a superstructure-based mixed-integer nonlinear programming method for optimal structural design including neuron number selection, pruning, and input selection for multilayer perceptron (MLP) ANNs. In addition, this method uses statistical measures such as the parameter covariance matrix in order to increase the test performance while permitting reduced training performance. The suggested approach was implemented on two public hyperspectral datasets (with 10% and 50% sampling ratios), namely Indian Pines and Pavia University, for the classification problem. The test results revealed promising performances compared to the standard fully connected neural networks in terms of the estimated overall and individual class accuracies. With the application of the proposed superstructural optimization, fully connected networks were pruned by over 60% in terms of the total number of connections, resulting in an increase of 4% for the 10% sampling ratio and a 1% decrease for the 50% sampling ratio. Moreover, over 20% of the spectral bands in the Indian Pines data and 30% in the Pavia University data were found statistically insignificant, and they were thus removed from the MLP networks. As a result, the proposed method was found effective in optimizing the architectural design with high generalization capabilities, particularly for fewer numbers of samples. The analysis of the eliminated spectral bands revealed that the proposed algorithm mostly removed the bands adjacent to the pre-eliminated noisy bands and highly correlated bands carrying similar information.

Abstract A superstructure-based approach was proposed for optimization of biorefineries that use Saccharina japonica as feedstock. The goal of this study was to determine the optimal flowsheet design to maximize the net present value by considering the mass and energy balance, capital and manufacturing costs. Multiple design alternatives reported in the literature were considered at each biorefinery processing stage, which transformed the superstructure optimization into a mixed integer nonlinear programming (MINLP) problem. In order to efficiently compute a solution for the resulting MINLP problem, the separable programming technique is employed by approximating the initial MINLP problem into a mixed integer linear programming (MILP) problem. The results indicated that the minimum ethanol selling price for optimal design is $1.97/gal, whereas the net present value of $61.5 million is obtained based on the current wholesale prices for both products and raw materials. Sensitivity analysis was performed to identify potential for economic improvement. The developed framework has the capacity to efficiently scan through processing alternatives to identify an economically optimal design for different potential objective functions. Graphical abstract Image 1 Highlights • A superstructure-based optimization model is developed. • Optimal flowsheet of macroalgae biorefinery are determined. • Two different optimization scenarios are investigated. • Sensitivity analysis elaborates potential improvements. [ABSTRACT FROM AUTHOR]

Highlights • Superstructure of integrated biorefinery in pulp and paper industry was developed. • It includes pulping, biochemical production and black liquor utilization sections. • Lignocellulosic biomass extracted from eucalyptus and bagasse are the feedstocks. • Potential of integrated biorefinery process was illustrated in three scenarios. • Integrated succinic acid and lactic acid can improve profitability. Abstract This work aims at developing sustainable processes for pulp and paper industry by integration of the biorefinery concept to an existing pulp and paper process. A systematic methodology employing a superstructure-based process synthesis approach is employed with support from computer-aided tools to determine potential pathways for a long-term sustainable growth objective. A superstructure of the multi-product biorefinery process network for the pulp and paper industry is developed. It is divided into three sub-networks, a chemical pulping section, a biochemical production section and a black liquor utilization section. Superstructure optimization is performed with the objective to maximize profit to determine optimal integrated networks for three scenarios. The obtained results provide useful insights for further development of the optimal networks as sustainable integrated biorefinery combined with pulp and paper mills. Graphical abstract Image, graphical abstract [ABSTRACT FROM AUTHOR]

Highlights • A novel quantitative measure of resilience for process systems is proposed. • Five resilience enhancement strategies are introduced for process systems. • A multiobjective nonlinear adaptive robust optimization model is developed. • The resulting problem is solved by a tailored global optimization algorithm. • Applications include a chemical process network and a shale gas processing system. Abstract This paper is concerned with the resilient design and operations of process systems in response to disruption events. A general framework for resilience optimization is proposed that incorporates an improved quantitative measure of resilience and a comprehensive set of resilience enhancement strategies for process design and operations. The proposed framework identifies a set of disruptive events for a given system, and then formulates a multiobjective two-stage adaptive robust mixed-integer fractional programming model to optimize the resilience and economic objectives simultaneously. The model accounts for network configuration, equipment capacities, and capital costs in the first stage, and the number of available processes and operating levels in each time period in the second stage. A tailored solution algorithm is developed to tackle the computational challenge of the resulting multi-level optimization problem. The applicability of the proposed framework is illustrated through applications on a chemical process network and a shale gas processing system. [ABSTRACT FROM AUTHOR]

A superstructure to produce added-value products (pigment, omega-3, glycerol, biodiesel, biogas, and fertilizers) from three species of microalgae (Chlorella vulgaris , Haematococcus pluvialis , Nannochloropsis spp.) is developed in this study. The superstructure is converted into a mixed-integer nonlinear programming (MINLP) model. A block integration approach is used to drastically decrease the CPU times by reducing the number of variables, parameters, and constraints. The model is solved with Baron/AOA in AIMMS software, and the most promising production pathway is identified. For all three biorefineries (cultivating different microalgae), the most promising production pathways (in terms of cost-effectiveness) remain consistent. These pathways involve an open pond, sedimentation and flotation, flocculation without any dryer, sonication, organic solvent pigment extraction, n-butanol solvent lipid extraction, lipid production, and anaerobic digestion. Changing technologies of dewatering stages (flocculation to centrifugation and filter press) proposes the second and third cost-effective production pathways. The most profitable biorefinery cultivates Haematococcus pluvialis , with annual profits of 62 $/kg of microalgae. A high amount of valuable pigment produced by Haematococcus pluvialis leads to 22 times higher profits than Chlorella vulgaris and 47 times higher than Nannochloropsis spp. The Haematococcus pluvialis biorefinery produces approximately 500 *103Kg of pigment bioproducts from 24 *106Kg biomass by using 200 *106Kg wastewater and 164 *106Kg of carbon dioxide, annually. Ultimately, a sensitivity analysis is executed to confirm how the production of pigment, the price of this bioproduct, and day/ night ratio affect the profitability of microalgae biorefineries. • A superstructure proposes three cost-effective pathways for production of bioproducts from microalgae. • A block integration strategy is developed to increase optimization speed. • Haematococcus pluvialis is the best microalgae for eco-friendly pigment production. • Profitability hinges on microalgae pigment content and sunlight duration. [ABSTRACT FROM AUTHOR]

Industrial operations consume energy and water in large quantities without accounting for potential economic and environmental burdens on future generations. Consumption of energy (mainly in the form of high pressure steam) and water (in the form of process water and cooling water) are essential to all processes and are strongly correlated, which requires development of systematic methodologies to address their interconnectivity. To this end, the subject of heat-integrated water allocation network design has received considerable attention within the research community in recent decades while further growth is expected due to imposed national and global regulations within the context of sustainable development. The overall mathematical model of these networks has a mixed-integer nonlinear programming formulation. As discussed in this work, proposed models in the literature have two main difficulties dealing with heat−water specificities, which result in complex formulations. These difficulties are addressed in this work through proposition of a novel nonlinear hyperstructure and a sequential solution strategy. The solution strategy is to solve three sub-problems sequentially and iteratively generate a set of potential solutions through the implementation of integer cut constraints. The novel mathematical approach also lends itself to an additional innovation for proposing multiple solutions balancing various performance indicators. This is exemplified with both a literature test case and an industrial-scale problem. The proposed solutions address a variety of performance indicators which guides decision-makers toward selecting the most appropriate configuration(s) among a large number of potential possibilities. Results exhibit that despite having a sequential solution strategy, better performance can be reached compared to previous approaches with the additional benefit of providing many potential solutions for further consideration by decision-makers to select the best case-specific solution.