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This study presents a superstructure framework to evaluate processing pathways for the production of omega-3 and pigments in an algae biorefinery. Different stages such as cultivation, harvesting, dewatering, drying, cell distribution, and extraction are considered as processing sections in this superstructure. To simplify and speed up modelling, each of these technologies is grouped in blocks. The superstructure framework is converted to a mixed-integer nonlinear programming (MINLP) model. It has more than 6.000 constraints/variables. The model is implemented in the Advanced Interactive Multidimensional Modelling System (AIMMS) software. The CPLEX and CONOPT are the selected solvers. The most promising pathways for three types of microalgae are proposed. These have differences in the dewatering section. Furthermore, the different pathways are compared in terms of cost and performance. The results show that the Haematococcus Pluvialis biorefinery leads to the highest profits due to pigments products' high amount and price.

International audience; Ammonia has a potential as carbon-free and high-energy density compound for chemical storage of renewable energies and its synthesis from green H2 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 €/tNH3 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 tNH3/day equals to 2.5 MW of stored power from 3 MW supplied as H2 to the process, with a total energy consumption of 10.67 kW h/kgNH3, including prior H2 and N2 production processes.

This work presents a novel methodology for integrated multi-objective superstructure optimization and multi-criteria assessment. The method is tailored for sustainable process synthesis utilizing mixed-integer linear programming (MILP). The six-step algorithm includes 1) superstructure formulation, 2) criteria definition and implementation, 3) criteria weighting, 4) single-criterion optimization, 5) reformulation and 6) multi-criteria optimization. It is automated in the Open sUperstrucTure moDelingand OptimizatiOnfRamework (OUTDOOR) and tested on integrated power-to-X and biomass-to-X processes for methanol production. Three criteria are considered, namely net production costs (NPC), net production greenhouse gas emissions (NPE) and net production fresh water demand (NPFWD). The optimization indicates NPC of 1307 €/tMeOH with NPE of -2.23 tœ/tMeOH and NPFWD of -3.42 tH2c/tMeOH for an optimal trade-off plant. The plant configuration features low-pressure alkaline electrolysis for hydrogen supply, absorption-based CO2 capture and steam production from methanol purge gas for internal heat supply. Conducted variation and sensitivity analyses indicate that methanol costs can drop to about 500 €/tMeOH if electricity is free of charge, or to 805 €/tMeOH if biogas is available at large quantities, if a least-cost process layouts are considered. However, all performed multi-criteria analyses imply a robust optimal process design utilizing electricity-based methanol production.

This work presents a novel methodology for integrated multi-objective superstructure optimization and multi-criteria assessment. The method is tailored for sustainable process synthesis utilizing mixed-integer linear programming (MILP). The six-step algorithm includes 1) superstructure formulation, 2) criteria definition and implementation, 3) criteria weighting, 4) single-criterion optimization, 5) reformulation and 6) multi-criteria optimization. It is automated in the Open sUperstrucTure moDelingand OptimizatiOnfRamework (OUTDOOR) and tested on integrated power-to-X and biomass-to-X processes for methanol production. Three criteria are considered, namely net production costs (NPC), net production greenhouse gas emissions (NPE) and net production fresh water demand (NPFWD). The optimization indicates NPC of 1307 €/tMeOH with NPE of -2.23 tœ/tMeOH and NPFWD of -3.42 tH2c/tMeOH for an optimal trade-off plant. The plant configuration features low-pressure alkaline electrolysis for hydrogen supply, absorption-based CO2 capture and steam production from methanol purge gas for internal heat supply. Conducted variation and sensitivity analyses indicate that methanol costs can drop to about 500 €/tMeOH if electricity is free of charge, or to 805 €/tMeOH if biogas is available at large quantities, if a least-cost process layouts are considered. However, all performed multi-criteria analyses imply a robust optimal process design utilizing electricity-based methanol production.

Can process intensification change the future of biodiesel? presents the answers for improving biodiesel production with process systems engineering tools such as membrane reactor modeling and superstructure optimization. A novel membrane reactor model with dynamic functions of reversible and irreversible fouling and a new dynamic membrane cleaning model have been developed for biodiesel production. The models can be used to capture the cyclic behavior of the membrane reactor and identify an optimal operating cycle that balances cleaning costs and production capacity. The superstructure model is a network of different alternative options and brings the optimization of biodiesel production from equipment level to process level. An optimal route to produce biodiesel from tallow with reactive distillation and heterogenous acid catalyst is found by solving the superstructure optimization problem. The impacts of different uncertainties such as the prices of feedstock and products, and the production capacity on the total profit of a biodiesel refinery are analyzed. From the technical assessment with the superstructure, process intensification technologies such as reactive distillation are important for biodiesel production in particular and the process industry in general. The thesis discusses the potential of process intensification and digital twin applications in the energy transition of the process industries. The digital twin offers high quality and dynamic models which can be used to develop, test, and improve not only the process intensification designs but also their operating conditions. Therefore, the combination reduces the investments for prototypes and pilot plants as well as the costs of adjusting the physical equipment, allowing a prosperous future for biodiesel.

Reverse electrodialysis (RED) is an emerging electro-membrane technology that generates electricity out of salinity differences between two solutions, a renewable source known as salinity gradient energy. Realizing full-scale RED would require more techno-economic and environmental assessments that consider full process design and operational decision space from the RED stack to the entire system. This work presents an optimization model formulated as a Generalized Disjunctive Programming (GDP) problem that incorporates a finite difference RED stack model from our research group to define the cost-optimal process design. The solution to the GDP problem provides the plant topology and the RED units´ working conditions that maximize the net present value of the RED process for given RED stack parameters and site-specific conditions. Our results show that, compared with simulation-based approaches, mathematical programming techniques are efficient and systematic to assist early-stage research and to extract optimal design and operation guidelines for large-scale RED implementation. This work was supported by the LIFE Programme of the European Union (LIFE19 ENV/ES/000143); the MCIN/AEI/10.13039/501100011033 and “European Union NextGenerationEU/PRTR” (PDC2021–120786-I00); and by the MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” (PRE2018–086454).

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 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.

To enhance the availability of superstructure optimization as a tool in process design for industries as well as science this work presents a fully Open sUperstrucTure moDeling and OptimizatiOn fRamework (OUTDOOR). This framework is written in Python using object-oriented programming in combination with algebraic modeling utilizing the PYOMO modeling language. In addition, an Excel-based data preparation tool, called Excel-Wrapper is presented. It provides an intuitive way to prepare process data and generate superstructures, which are ready-made for solving using open-source as well as commercial optimization solvers.

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.

This work presents a mixed-integer linear programming (MILP) heat integration model tailored to superstructure optimization. It includes energy targeting based on a transshipment model combined with linearized heat exchanger capital costs. A variable number of heat utilities at different temperature levels can be implemented. In addition, the model facilitates the utilization of high temperature heat pumps to benefit from low exergy waste heat. The heat integration model is part of the Open sUperstrucTure moDeling and OptimizatiOn fRamework (OUTDOOR) and thus can be accessed easily using its intuitive excel-based interface. A model evaluation of the MILP shows low cost deviations of 1–14 % compared to more complex models, with fast solution times. Additionally, a practical superstructure case study is presented, where internal heat recovery reduces the external heat consumption of a power-to-methanol process by 40 %, thus underlining the relevance for adequate consideration.

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. Copyright © 2022 Gargalo, Rapazzo, Carvalho and Gernaey .

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.

Recent advances in metabolic engineering have enabled the production of chemicals via bio-conversion using microbes. However, downstream separation accounts for 60–80% of the total production cost in many cases. Previous work on microbial production of extracellular chemicals has been mainly restricted to microbiology, biochemistry, metabolomics, or techno-economic analysis for specific product examples such as succinic acid, xanthan gum, lycopene, etc. In these studies, microbial production and separation technologies were selected apriori without considering any competing alternatives. However, technology selection in downstream separation and purification processes can have a major impact on the overall costs, product recovery, and purity. To this end, we apply a superstructure optimization based framework that enables the identification of critical technologies and their associated parameters in the synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions. We divide extracellular chemicals into three categories based on their physical properties, such as water solubility, physical state, relative density, volatility, etc. We analyze three major extracellular product categories (insoluble light, insoluble heavy and soluble) in detail and provide suggestions for additional product categories through extension of our analysis framework. The proposed analysis and results provide significant insights for technology selection and enable streamlined decision making when faced with any microbial product that is released extracellularly. The parameter variability analysis for the product as well as the associated technologies and comparison with novel alternatives is a key feature which forms the basis for designing better bioseparation strategies that have potential for commercial scalability and can compete with traditional chemical production methods.

This work presents a fully open-source superstructure optimization model for bi-criteria process design optimization. This model is implemented in the Open sUperstrucTure moDeling and OptimizatiOn fRamework (OUTDOOR). It combines mass, energy and cost balances with a more sophisticated heat integration concept with low computational effort and acceptable accuracy. The model is applied to a Power-to-Methanol (PtM) process design case study. A cost optimal methanol plant is identified at net production costs (NPC) of 892 €/tMeOH and net production emissions (NPE) of -1.937 tCO2-eq./tMeOH. It utilizes CO2 captured from refinery flue gas and hydrogen supply via. ambient pressure alkaline electrolysis. A plant configuration, designed for minimum CO2 emissions yields costs of 979 €/tMeOH with emissions of -2.191 tCO2-eq./tMeOH, using CO2 from ambient air and refinery and cement factory flue gases. A sensitivity analysis on electricity prices forecasts cost competitive methanol at large production capacities and low electricity prices of 2 ct/kWh.

To enhance the availability of superstructure optimization as a tool in process design for industries as well as science this work presents a fully Open sUperstrucTure moDeling and OptimizatiOn fRamework (OUTDOOR). This framework is written in Python using object-oriented programming in combination with algebraic modeling utilizing the PYOMO modeling language. In addition, an Excel-based data preparation tool, called Excel-Wrapper is presented. It provides an intuitive way to prepare process data and generate superstructures, which are ready-made for solving using open-source as well as commercial optimization solvers.

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, gasification etc 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.

International audience; In this paper, we survey the main superstructure-based approaches in process system engineering, with a particular emphasis on the existing literature for automated superstructure generation. We examine both classical and more recent representations in terms of generality, ease of use, and tractability. We also discuss the implications that different representations may have on strategies for algebraic modeling and optimization. We then review the state-of-the-art in software implementations to support synthesis. Finally, we examine the use of evolutionary—recently referred to as superstructure-free—approaches, in which algorithmic procedures dynamically generate and evaluate candidate process structures.

As breakthrough cellular therapy discoveries are translated into reliable, commercializable applications, effective stem cell biomanufacturing requires systematically developing and optimizing bioprocess design and operation. This article proposes a rigorous computational framework for stem cell biomanufacturing under uncertainty. Our mathematical tool kit incorporates: high‐fidelity modeling, single variate and multivariate sensitivity analysis, global topological superstructure optimization, and robust optimization. The advantages of the proposed bioprocess optimization framework using, as a case study, a dual hollow fiber bioreactor producing red blood cells from progenitor cells were quantitatively demonstrated. The optimization phase reduces the cost by a factor of 4, and the price of insuring process performance against uncertainty is approximately 15% over the nominal optimal solution. Mathematical modeling and optimization can guide decision making; the possible commercial impact of this cellular therapy using the disruptive technology paradigm was quantitatively evaluated. © 2017 American Institute of Chemical Engineers AIChE J, 64: 3011–3022, 2018

This paper presents a novel methodology for the optimisation of the preliminary design of heat-integrated multicomponent distillation sequences. This is achieved through use of a reduced process superstructure where the role of splitting each adjacent key component pair is assigned to an individual separation column, greatly reducing the size and complexity of the problem. This methodology uses information about other process streams on site with which heat can be exchanged within the initial design in order to find a plant-wide optimal separation configuration with the aim of reducing the cost of heating provided by utilities including those used for heating and cooling process streams. In order that this model can be formulated as a mixed-integer linear program, this model utilises a discretised temperature grid where stream temperatures are allowed to vary. This methodology was tested on an example of a mixed alkane feed stream, with the sequence changing dependent on the degree of process integration. The method was found to have the potential for significant cost reductions compared to a heuristic design, with the example exhibiting a cost saving of over 50% and a reduction in CO2 associated with process heating of almost 60%, though the magnitude of these savings is highly dependent on the specific example to which it is applied.

Sustainable polymers derived from biomass have great potential to replace petrochemical based polymers and fulfill the ever-increasing market demand. To facilitate their industrialization, in this research, a comprehensive superstructure reaction network comprising a large number of reaction pathways from biomass to both commercialized and newly proposed polymers is constructed. To consider economic performance and environmental impact simultaneously, both process profit and green chemistry metrics are embedded into the multiobjective optimization framework, and MINLP is used to enable the effective selection of promising biopolymer candidates. Through this proposed approach, this study identifies the best biopolymer candidates and their most profitable and environmentally friendly synthesis routes under different scenarios. Moreover, the stability of optimization results regarding the price of raw materials and polymers and the effect of process scale on the investment cost are discussed in detail. These results, therefore, pave the way for future research on the production of sustainable biopolymers.

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., AIChE Journal, 66 (5), ISSN:1547-5905, ISSN:0001-1541

Advances in simulation and optimization technologies coupled with the continued growth in computing power now increasingly pave the way for the development of advanced model-based engineering design frameworks. In this work, we propose an extensive computational framework, which brings together state-of-the-art engineering practices, such as high fidelity process simulation, superstructure-based conceptual design, global sensitivity analysis, Monte Carlo procedures for uncertainty quantification, and a stochastic simulation-based design space optimizer in order to foster decision making under uncertainty. The capabilities of the framework are highlighted in a case study, which addresses the challenges of how to synthesize and design wastewater treatment plant configurations under influent uncertainties. In order to handle multiple stochastic constraints, a black-box solver using a new infill criterion for surrogate-based optimization is also proposed. The results demonstrate the promising potential of the simulation and sampling-based framework for effectively addressing stochastic design problems arising in broader engineering domains.

A key approach in expediting the transition towards a bio-based economy is the conceptual design and implementation of integrated second-generation biorefineries (iSGB). A conceptual design approach for these iSGBs is Superstructure Optimization (SSO), which yields an optimal candidate process topology (CPT) [1]. By following a simulation-based optimization (SBO) approach, complex models including inherent uncertainties can be employed in the search for an optimal process design [2]. We therefore propose a novel synergistic framework for the synthesis and design of iSGBs: based on a hybrid approach integrating surrogate-based superstructure optimization (SSO) with simulation-based design optimization (SBO). As a proof of concept, we apply this framework to a case study to the process synthesis and design of a xylitol biorefinery. Xylitol is a platform chemical and is used as sugar substitute in food industries. It can be produced by microbial fermentation from lignocellulosic biomass, which makes it an ideal product for an iSGB [3]. Consequently, succinic acid, another high-potential platform chemical- is selected as value-added co-product [4]. In order to meet the high energy demand of the iSGB, the lignin fraction is chosen as substrate for a combustion process. Mechanistic models for all necessary unit operations are developed and assessed by uncertainty and sensitivity analysis. Surrogate models are utilized for the composition of the superstructure. Performing SSO yields several CPTs, which are then subjected to SBO including the uncertainties in the model. The result of the application of the presented framework is then a consolidated base-case process for a xylitol biorefinery, which can be easily extended in the superstructure towards the evaluation of further products, process integration, plant-wide optimization or value chain optimization. The resulting process itself is evaluated against both criteria of being economically viable and sustainable

The past decades have seen a diversification of the sugarcane industry with the emergence of new technology to produce bioenergy from by-product and waste process streams. Given Brazil’s ambitious goal of reducing green-house gas emissions by over 40% below 2005 levels by 2030, it is of paramount importance to develop reliable decision-making systems in order to stimulate investment in these low-carbon technologies. This paper seeks to develop a more accurate optimization model to inform risk-conscious investment decisions for bioenergy generation capacity in sugarcane mills. The main objective is for the model to enable a better understanding of how Brazilian government policies, such as the electricity price in the regulated market, may impact these investments, by taking into account the uncertainty in sugar, ethanol and spot electricity markets and the interdependency between production and investment decisions in terms of saleable product mix. The proposed methodology combines portfolio optimization theory with superstructure process modeling and it relies on simple surrogates derived from a detailed sugarcane plant simulator to retain computational tractability and enable scenario analysis. The case study of an existing sugarcane plant is used to demonstrate the methodology and illustrate how the model can assist decision-makers. In all of the scenarios assessed, the model recommends investment in extra bioelectricity capacity via the anaerobic digestion of vinasse but advises against investment in second-generation ethanol production via the hydrolysis of surplus bagasse. Furthermore, the decision to upgrade the cogeneration system with a condensation turbine is highly sensitive to the electricity price practiced in the regulated market, capacity constraints on the sugar-ethanol mix, and the accepted level of risk. Another key insight drawn from the case study is that recent market conditions have favored a production focused on the sugar business, making it challenging for policy-makers to create attractive scenarios for biofuels. Long-term electricity contracting appears to be the main hedging strategy for de-risking other products and investments in the sugarcane business, provided it is priced adequately.

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.

Genome-scale models have become indispensable tools for the study of cellular growth. These models have been progressively improving over the past two decades, enabling accurate predictions of metabolic fluxes and key phenotypes under a variety of growth conditions. In this work, an efficient computational method is proposed to incorporate genome-scale models into superstructure optimization settings, introducing them as viable growth models to simulate the cultivation section of biorefinaries. We perform techno-economic and life-cycle analyses of an algal biorefinery with five processing sections to determine optimal processing pathways and technologies. Formulation of this problem results in a mixed-integer nonlinear program, in which the net present value is maximized with respect to mass flowrates and design parameters. We use a genome-scale metabolic model of Chlamydomonas reinhardtii to predict growth rates in the cultivation section. We study algae cultivation in open ponds, in which exchange fluxes of biomass and carbon dioxide are directly determined by the metabolic model. This formulation enables the coupling of flowrates and design parameters, leading to more accurate cultivation productivity estimates with respect to substrate concentration and light intensity.

У статті представлено метод оптимізації технологічних схем водного господарства, що базується на економічних критеріях. Ряд процесів водоспоживання характеризується безперервною і стрімкою зміною основних параметрів (зокрема, ступеню забрудненості та витрати води). Пропонований метод проектування враховує подібні зміни, внаслідок чого отримана схема водного господарства підготовлена до роботи в таких умовах (тобто, є гнучкою). В основі представленого методу гнучкого проектування схем водоспоживання лежать принципи адитивної комбінаторики, зокрема, теорема про кутові точки. The paper presents the process water network cost optimization method. The purity and flow rate parameters for some processes change continuously and/or by leaps and bounds. The proposed design method takes into account these changes, so the obtained water network is prepared to work in such conditions (it is flexible). The flexible network design method was based on the theorem of corner points solutions. В статье представлен базирующийся на экономических критериях метод оптимизации технологических схем водного хозяйства. Ряд процессов водопотребления характеризуются непрерывным и стремительным изменением основных параметров (в частности, степени загрязненности и расхода воды). Предлагаемый метод проектирования учитывает подобные изменения, вследствие чего полученная схема водного хозяйства подготовлена к работе в таких условиях (то есть, является гибкой). В основе представленного метода гибкого проектирования схем водопотребления лежат принципы аддитивной комбинаторики, в частности, теорема об угловых точках.

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.

Sustainable polymers derived from biomass have great potential to replace petrochemical based polymers and fulfill the ever-increasing market demand. To facilitate their industrialization, in this research, a comprehensive superstructure reaction network comprising a large number of reaction pathways from biomass to both commercialized and newly pro- posed polymers is constructed. To consider economic performance and environmental impact simultaneously, both pro- cess profit and green chemistry metrics are embedded into the multiobjective optimization framework, and MINLP is used to enable the effective selection of promising biopolymer candidates. Through this proposed approach, this study identifies the best biopolymer candidates and their most profitable and environmentally friendly synthesis routes under different scenarios. Moreover, the stability of optimization results regarding the price of raw materials and polymers and the effect of process scale on the investment cost are discussed in detail. These results, therefore, pave the way for future research on the production of sustainable biopolymers..

The growing desire to improve resource efficiency and environmental impact of industrial processes is directly linked to optimal management of heat, mass and power flows. The concept of industrial symbiosis tackles this issue by proposing interplant heat recovery and resource transfer which can bring economical and environmental benefits to each party. A comprehensive methodology is required which can easily be incorporated in the planning of industrial clusters. Therefore, a generic hybrid mixed integer linear programming superstructure has been developed to address simultaneous heat, water, and power optimization in interplant operations. Additional concepts are included in the previously-proposed water network superstructure (Kermani et al., 2017) to account for the issues related to interplant heat and mass exchange. A cold utility superstructure is included in the water network while a steam network superstructure is modified to better represent the feedwater heaters and heat recovery opportunities. The proposed methodology is applied to an industrial case study. Results exhibit a large potential for synergies among industrial sites, even in disparate sectors, and emphasize the importance of a generic approach.

Model-based design of separation processes for heterogeneous azeotropic mixtures is a challenging task. The multiplicity of the solutions of equilibrium calculations and the discontinuity due to the potential switching between homogeneous and heterogeneous mixtures on a tray severely complicate the application of deterministic optimization. This paper presents a novel computational approach to reliably determine phase stability and the correct equilibrium solutions in every iteration of the optimization. The approach builds on the decomposition of the optimization problem into a generic superstructure model and an implicit model for equilibrium and enthalpy calculations, which is integrated into the optimization problem by means of an external function. The phase states and equilibrium solutions are determined by means of a reliable homotopy continuation algorithm. An additional reformulation of equilibrium solutions and enthalpy calculations allows overcoming the discontinuity problems. Different case studies illustrate the applicability and show the potential of the proposed method.

Capital investment, next to the product demand, sales, and production costs, is one of the key metrics commonly used for project evaluation and feasibility assessment. Estimating the investment costs of a new product/process alternative during early-stage design is a challenging task, which is especially relevant in biorefinery research where information about new technologies and experience with new technologies is limited. A systematic methodology for uncertainty analysis of cost data is proposed that employs: (a) bootstrapping as a regression method when cost data are available; and, (b) the Monte Carlo technique as an error propagation method based on expert input when cost data are not available. Four well-known models for early-stage cost estimation are reviewed and analyzed using the methodology. The significance of uncertainties of cost data for early-stage process design is highlighted using the synthesis and design of a biorefinery as a case study. The impact of uncertainties in cost estimation on the identification of optimal processing paths is indeed found to be profound. To tackle this challenge, a comprehensive techno-economic risk analysis framework is presented to enable robust decision-making under uncertainties. One of the results using order-of-magnitude estimates shows that the production of diethyl ether and 1,3-butadiene are the most promising with the lowest economic risks (among the alternatives considered) of 0.24 MM$/a and 4.6 MM$/a, respectively.

This paper introduces a novel MILP approach for the design of distillation columns sequences of zeotropic mixtures that explicitly include from conventional to fully thermally coupled sequences and divided wall columns with a single wall. The model is based on the use of two superstructure levels. In the upper level a superstructure that includes all the basic sequences of separation tasks is postulated. The lower level is an extended tree that explicitly includes different thermal states and compositions of the feed to a given separation task. In that way, it is possible to a priori optimize all the possible separation tasks involved in the superstructure. A set of logical relationships relates the feasible sequences with the optimized tasks in the extended tree resulting in a MILP to select the optimal sequence. The performance of the model in terms of robustness and computational time is illustrated with several examples. Spanish Ministry of Science and Innovation (CTQ2012-37039-C02-02).

This work analyzes the synergy between two complementary unit operations - adsorbent dehumidification and drying - and presents a mixed integer nonlinear programming approach to optimize energy performance in a two-stage system. Combined with active constraint analysis, the adsorbent properties that promote energy performance are derived. Microporous adsorbents with higher sorption capacities at low vapor pressures and requiring higher regeneration temperatures are preferred for ambient air dehumidification in the first stage. For exhaust air dehumidification, mesoporous adsorbents with lower regeneration temperatures are preferred such that the exhaust air from the first regeneration stage can sufficiently regenerate them. For drying below 50 C, energy consumption reductions of about 70% are achieved compared to conventional dryers without adsorbent dehumidification depending on adsorbent properties. The results demonstrate the usefulness of superstructure optimization in matching the drying process with the capabilities of the adsorbents to enhance process synergy for improved energy efficiency. © 2013 American Chemical Society.

This work analyzes the synergy between two complementary unit operations - adsorbent dehumidification and drying - and presents a mixed integer nonlinear programming approach to optimize energy performance in a two-stage system. Combined with active constraint analysis, the adsorbent properties that promote energy performance are derived. Microporous adsorbents with higher sorption capacities at low vapor pressures and requiring higher regeneration temperatures are preferred for ambient air dehumidification in the first stage. For exhaust air dehumidification, mesoporous adsorbents with lower regeneration temperatures are preferred such that the exhaust air from the first regeneration stage can sufficiently regenerate them. For drying below 50 C, energy consumption reductions of about 70% are achieved compared to conventional dryers without adsorbent dehumidification depending on adsorbent properties. The results demonstrate the usefulness of superstructure optimization in matching the drying process with the capabilities of the adsorbents to enhance process synergy for improved energy efficiency. © 2013 American Chemical Society.

In this paper, a new formulation based on disjunctive programming to model the superstructure of alternative configurations for the synthesis, design and analysis of combined cycle power and desalination plant recently developed in Mussati et al. [Desalination, 182 (2005) 123?129] is presented. In this new formulation, boolean variables model discrete decisions while continuous variables represent the operation conditions of the process, e.g., flow rates, energy demand. Optimal unit configuration and operating conditions are computed by solving the proposed model in order to satisfy electricity generation and freshwater productions demands. Rigorous, non-convex and highly non-linear constraints are involved in the formulation, therefore, robust and efficient solution algorithms have to be used. The Logic-Based Outer Approximation (LOA) algorithm developed by Turkay and Grossmann [Comp. Chem. Eng., 20 (8) (1996) 959] with the modifications introduced by Yeomans and Grossmann [Ind. Eng. Chem. Res., 39 (6) (2000) 1637] are used as part of the solution procedure. The model is implemented and solved in the General Algebraic Modeling System (GAMS). Several study cases for different required power to water ratios are presented and analyzed in order to illustrate the robustness and computational performance of the proposed model. Fil: Mussati, Sergio Fabian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Barttfeld, M.. Universitat Erlangen-Nuremberg; Alemania Fil: Aguirre, Pio Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina Fil: Scenna, Nicolas Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina

A multistage multilayer systematic procedure has been developed for the selection of the optimal product portfolio from waste biomass as feedstock for systems involving water–energy–food nexus. It consists of a hybrid heuristic, metric-based, and optimization methodology that evaluates the economic and environmental performance of added-value products from a particular raw material. The first stage preselects the promising products. Next, a superstructure optimization problem is formulated to valorize or transform waste into the optimal set of products. The methodology has been applied within the waste to power and chemicals initiative to evaluate the best use of the biomass residue from the olive oil industry toward food, chemicals, and energy. The heuristic stage is based on the literature review to analyze the feasible products and techniques. Next, simple metrics have been developed and used to preselect products that are promising. Finally, a superstructure optimization approach is used to design the facility that processes leaves, wood chips, and olives into final products. The best technique to recover phenols from “alperujo”, a wet solid waste/byproduct of the process, consists of the use of membranes, while the adsorption technique is used for the recovery of phenols from olive leaves and branches. The investment required to process waste adds up to €110.2 million for a 100 kt/yr for the olive production facility, while the profit depends on the level of integration. If the facility is attached to an olive oil production, the generated profit ranges between 14.5 MM €/yr (when the waste is purchased at prices of €249 per ton of alperujo and €6 per ton of olive leaves and branches) and 34.3 MM €/yr when the waste material is obtained for free., Methodology and case study for the selection of added-value products out of waste and process design.

This work deals with the design of integrated facilities for the production of xylitol and sorbitol from lignocellulosic biomass. Xylitol can be obtained from xylose via fermentation or catalytic hydrogenation. Sorbitol is obtained from glucose, but preferably from fructose, and also via fermentation or catalytic hydrogenation. Fructose can be obtained from glucose via isomerization. Thus, a superstructure of alternatives is formulated to process switchgrass, corn stover, miscanthus, and other agricultural and forestry residues. Different pretreatments, such as dilute acid or ammonia fiber explosion (AFEX), for the fractionation of the biomass are evaluated. Next, after hydrolysis, the C5 and C6 sugars are processed separately for which a catalytic or a fermentation stage are considered. Glucose has to be isomerized before it can be processed. Finally, crystallization in a multistage evaporator system is used for purification. The optimization of the system is done by the use of dilute acid and the catalytic system. A system of 3 crystallizers is selected. For a facility that produces 145 kt/yr of xylitol and 157.6 kt/yr of sorbitol, the investment adds up to 120.74 M€ for a production cost of 0.28 €/kg products. The inverse engineering of biomass was also performed resulting in a composition of 15% water, 20% cellulose, 40% hemicellulose, 15% lignin, and 5% ash. The closest biomass corresponds to Sargassum (brown algae), which is capable of producing 230.5 kt/yr of xylitol and 116 kt/yr of sorbitol with investment and production costs of 120.5 M€ and 0.25 €/kg products, respectively.

Industries consume large quantities of energy and water in their processes which are often considered to be peripheral to the process operation. Energy is used to heat or cool water for process use; additionally, water is frequently used in production support or utility networks as steam or cooling water. This enunciates the interconnectedness of water and energy and illustrates the necessity of their simultaneous treatment to improve energy and resource efficiency in industrial processes. Since the seminal work of Savulescu and Smith in 1998 introducing a graphical approach, many authors have contributed to this field by proposing graphically- or optimization-based methodologies. The latter encourages development of mathematical superstructures encompassing all possible interconnections. While a large body of research has focused on improving the superstructure development, solution strategies to tackle such optimization problems have also received significant attention. The goal of the current article is to study the proposed methodologies with special focus on mathematical approaches, their key features and solution strategies. Following the convention of Jezowski, solution strategies are categorized into: decomposition, sequential, simultaneous, meta-heuristics and a more novel strategy of relaxation/transformation. A detailed, feature-based review of all the main contributions has also been provided in two tables. Several gaps have been highlighted as future research directions.

This paper presents a method for phenomena-based process synthesis that automatically generates flowsheet variants by means of superstructure optimization. By composing processes of phenomena instead of predefined unit operations, the creativity during process synthesis is maximized and process intensification principles can be considered even beyond already existing equipment. The optimization-based method generates promising phenomena-based flowsheet variants which can further be interpreted and translated into equipment by using databases or proposing new equipment that implements the phenomena-based design. The applicability of the developed method is demonstrated for the case study of ethanol dehydration. The method automatically generates conventional as well as innovative flowsheet variants, some of which even show an improved performance estimate.

Efficient consumption of energy and material resources, including water, is the primary focus for process industries to reduce their environmental impact. The Conference of Parties in Paris (COP21) highlighted the prominent role of industrial energy efficiency in combatting climate change by reducing greenhouse gas (GHG) emissions. Consumption of energy and material resources, especially water, are strongly interconnected; and therefore, must be treated simultaneously using a holistic approach to identify optimal solutions for efficient processing. Such approaches must consider energy and water recovery within a comprehensive process integration framework which includes options such as organic Rankine cycles for electricity generation from low to medium temperature heat. This thesis addresses the issue of how to efficiently manage energy and water in industrial processes by presenting two systematic methodologies for the simultaneous optimization of heat and mass and combined heat and power production. A novel iterative sequential solution strategy is proposed for optimizing heat-integrated water allocation networks through decomposing the overall problem into three sub-problems using mathematical programming techniques. The approach is capable of proposing a set of potential energy and water reduction opportunities that should be further evaluated for technical, economical, physical, and environmental feasibilities. A novel and comprehensive superstructure optimization methodology is proposed for organic Rankine cycle (ORC) integration in industrial processes including architectural features, such as turbine-bleeding, reheating, and transcritical cycles. Meta-heuristic optimization (via a genetic algorithm) is combined with deterministic techniques to solve the problem: by addressing fluid selection, operating condition determination, and equipment sizing. This thesis further addresses the importance of holistic approaches by applying the proposed methodologies on a kraft pulp mill. In doing so, freshwater consumption is reduced by more than 60%, while net power output is increased by a factor of six. The results exhibit that interactions among these elements are complex and therefore underline the necessity of such methods to explore their optimal integration with industrial processes. The potential implications of this work are broad, extending from total site integration to industrial symbiosis.