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Reverse electrodialysis (RED), an emerging electrochemical technology that uses ion-selective membranes to directly draw electricity out from salinity differences between two solutions, i.e., salinity gradient energy (SGE), has the potential to be a clean and steady renewable source to reach a sustainable water and energy supply portfolio. Although RED has made notable advances, full-scale RED progress demands more techno-economic and environmental assessments that consider full process design and operational decision space from module- to system-level. This work presents an optimization model formulated as a Generalized Disjunctive Programming (GDP) problem to define the cost-optimal RED process design for different deployment scenarios. We use a predictive model of the RED stack developed and validated in our research group to fully capture the behavior of the system. The problem addressed is to determine the RED plant's topology and the working conditions for a given design of each RED stack which renders the cost-optimal design for the defined problem and scenario. Our results show that, compared with simulation-based approaches, mathematical programming techniques are an efficient and systematic approach to provide decision-making support in early-stage applied research and to obtain design and operation guidelines for full-scale RED implementation in real scenarios. Project LIFE19 ENV/ES/000143 funded by the LIFE Programme of the European Union. Grant PDC2021-120786- I00 funded by MCIN/AEI/10.13039/501100011033 and by the “European Union NextGenerationEU/PRTR”. Grant PRE2018-086454 funded by MCIN/AEI/10.13039/501100011033 and by “ESF Investing in your future”.

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.

Synthesis of biorefinery processes have been of great interest in the research community and industrial practitioners for the past two decades. However, there is a lack of systematic insight-based approach for the synthesis of biorefinery processes, especially at the conceptual design level. This paper presents an extension on the systematic hierarchical decomposition approach (commonly known as the “Douglas’s hierarchical approach”) for the synthesis of biorefinery processes. Note that the Douglas’s hierarchical approach was originally developed for the synthesis of conventional chemical processes, and hence has various limitations when it is applied directly for the synthesis of biorefinery processes. These include unknown reaction stoichiometry, wide varieties of biomass feedstock, alternative conversion platforms, uncertain characteristic of biomass feedstock, etc. These challenges are now being addressed in the extensions proposed in this work for biorefinery process synthesis. Besides, additional tasks are included to assist reaction pathways screening at the early stage of design, while solid recovery system and material integration are being considered within the extended approach to maximise resource conservation and to minimise waste generation. The extended Douglas’s approach is demonstrated with a case study on the synthesis of palm-based biorefinery processes.

The recent Hydrogen Refueling Station (HRS) explosion in Norway confirms the need for improved design of these facilities to further facilitate the commercialization of a Hydrogen economy. Currently HRS designs are primarily based on the consideration of economics to supply Hydrogen at a competitive price and their safety is evaluated through Quantitative Risk Assessment as dictated by the codes and the standards. However, the absence of relevant safety perspective in the early design stage itself leads to a possibility of HRS being overdesigned in terms of safety. In this study, we propose an integrated model using queuing theory, process synthesis, QRA and economic analysis for designing HRS. The application of the integrated model is demonstrated using the inherently safer design philosophy. For the base design under consideration, it was observed that reducing liquid storage capacity can significantly reduce the risk associated with explosion along with an improvement in HRS economics, while reducing dispenser hose diameter can reduce the risk associated with jet-fire with a slight detriment to HRS economics.

The P-graph framework was developed to solve Process Network Synthesis (PNS) problems in plant design. It provides a powerful engineering toolbox based on the constituent Maximal Structure Generation (MSG), Solution Structure Generation (SSG) and Accelerated Branch-and-Bound (ABB) algorithms. During the four decades of its development, the P-graph framework has proven to be capable of solving various engineering applications framed as PNS problems. This review paper gives a survey of the development of the P-graph framework, with emphasis on its enhancements and the diversification of applications in process engineering. Research gaps and promising directions for P-graph research are then discussed.

The production of drop-in replacements for biofuels is a grand challenge. Lignocellulosic biomass is considered a promising feedstock for producing fuel ethanol because of its great availability. Restrepo-Florez et al. systematically study the upgrading of ethanol toward advanced fuels with tailored properties and with consideration of process synthesis.

Heat-integrated water network (HIWN) is an active area of research in the past decade. It focuses on simultaneous saving of energy and water, which are both important resources to enhance sustainability for the process plants. This problem has been previously approached using insight-based pinch analysis and mathematical programming techniques. In this work, an alternative optimization tool, process graph (P-graph) framework is utilized to solve for water and energy integration. P-graph is a graph-theoretic approach to process synthesis, for which the developed algorithms can reduce the complexity of the mathematical programming algorithms efficiently. P-graph methodology enables the effective generation of sub-optimal network structures that allow wider exploration of alternatives through network topology. This paper also introduces some transformation techniques to convert the non-linear programming (NLP) model in the non-isothermal HIWN problem into model which is solvable using P-graph. Two examples with different water recovery schemes are used to elucidate the proposed approach, covering both direct recycle/reuse and regeneration networks; for both cases, isothermal and non-isothermal mixing problems are solved.

Process intensification (PI) has been gaining increasing momentum in the chemical engineering research community and the chemical/energy industry. While many PI alternative technologies and their conventional counterparts exist, systematic approaches and tools to decide on the most promising intensified process solutions are currently rather lacking. Process Systems Engineering (PSE) can contribute uniquely to this challenge by leveraging process synthesis, design, analysis, and optimization tools. However, key open questions in this area still remain, including: (i) how to efficiently address the combinatorial design space and systematically deliver intensified designs, and (ii) how to ensure the operablility performances of the derived intensified structures at an early design stage. This paper provides a brief review of recent progress towards the synthesis of operable process intensification systems, highlights major challenges, and discusses future research opportunities.

Dealing with uncertainty is one of practical issues in design and operation of heat exchanger networks (HENs), arising the problem of flexible HEN synthesis. This paper addresses the state-of-the-art methods for flexible HEN synthesis based on sensitivity analysis, resilience analysis, flexibility analysis and multiperiod synthesis techniques as well. Each of these methods is summarized by presenting their general procedures and recent developments on modeling, solving strategies and applications. Some current topics related to flexible process synthesis have been briefly presented to provide several future research possibilities.

For our works, Mass Exchangers (MEs) are membrane equipment that exchange some component between two streams in a countercurrent arrangement. In previous works these were proposed to use them (with the goal of hydrogen recovery, energy saving and waste minimization) at different stages of the hierarchical decision procedure for process synthesis by Douglas: at an early design stage, when deciding the recycle structure of the process (resulting in considerable changes in the process structure); or at a final design stage, before deciding the mass and energy integrations of process streams (resulting in minor changes in the process structure).The heuristic allocation of MEs was used independently at both design stages in previous works, with the goal of comparing with other design alternatives reported in the literature that use membranes in the conventional configuration (one feed stream and two exit streams; retentate and permeate). In contrast, the present work compares the results of using MEs, when they are used individually at both design stages, and when they are used jointly at both design stages, in different configurations. For the case study example (the HDA Process), the use of a ME at an early design stage reduces the fresh hydrogen consumed by 3.37%, and the recycle compressor power by 4.09%. The use of a ME at a final design stage reduces the fresh hydrogen consumed by 31.64%, but it does not reduce the recycle compressor power. The joint use of the MEs at both design stages reduces the fresh hydrogen consumed by 14.54%, and the recycle compressor power by 2.91%.The joint use of MEs at both design stages retains the principal benefits of its use at early and final design stages (energy saving and hydrogen recovery, principally), so when both a reduction in the fresh hydrogen consumed and in the recycle compressor power is desired, it is the most appropriate option. Fil: Fischer, Carlos Daniel. 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: Iribarren, Oscar Alberto. 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

Adsorption is an extensively used technique in wastewater treatment because this process is low cost, practical in operation and efficient. However, the regeneration of spent adsorbents is usually restricted by the strong interactions between the surface of adsorbents and adsorbates. Costs of water purification can be reduced significantly if the spent adsorbents can be regenerated and reused for several cycles. Hence, one alternative to face this issue is based on the formulation of adsorbent in a form of coating by improving its function so that it will be feasible to perform the regeneration. In this study, we have improved a coating formulation by modifying it with an iron salt to carry out the photo-Fenton process in the presence of H2O2 and light assistance. Results showed that the adsorption of Acid Red 1 on an iron modified composite adsorbent coating was effective and able to achieve up to ten adsorption-regeneration cycles via photo-Fenton process. A statistical physics model was applied to understand the dye adsorption mechanism. This model was utilized to estimate the adsorption geometry of this dye and to calculate the adsorption capacities at different temperatures. This statistical physics model relied on an energetic consideration in order to describe the interaction between the dye molecule and iron-modified composite adsorbent coating surface.

The production and demand for plastic products is set to only increase in the near future, despite efforts to curtail this demand. This increased demand and production comes with an increase in plastic waste, which already sees millions of tonnes discarded into landfills every year. Growing concern over the effects of this waste on the world's ecosystems has led to urgent interest in technologies for chemically converting waste plastic into other products. This manuscript uses process synthesis techniques to analyse and compare the relative performance of two commonly used chemical conversion processes: polyethylene pyrolysis and polyethylene gasification. This analysis technique is unique in that energy forms the basis of the analysis but also allows environmental and economic concerns to be considered simultaneously. It was found that pyrolysis processes are more in line with the goal of a cyclic economy for waste plastic, but that gasification processes can offer higher revenue through the production of alternate chemical products. The potential profits generated from these waste plastic processing strategies was found to be between 50 and 360 USD/ton of polyethylene, demonstrating a high economic value on what is, at present, a waste product.

The concept of process intensification (PI) focusses on the creation of safe and compact energy and cost effective technologies and processes for a more sustainable chemical industry. It has let to the development of many innovative technologies and process concepts, which gain increasing interest in academia and industry. While PI provides tremendeous opportunities, it also presents a significant challenge for process engineers, as they have to handle a steadily increasing portfoliot of options during process synthesis, as well as the design of highly integrated process configurations. The current article summarizes recent developments in process systems engineering (PSE) that allow for an efficient and systematic integration of PI concepts in process synthesis and design. It further points out future perspectives and the need for a stronger collaboration between industry and academia in order to bring the developments from PSE and PI into application.

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.

Translation of catalysts developed in academia to industrial end-users remains a challenge due to a lack of knowledge about the impact of catalyst attributes on the whole process and vice versa. A systematic methodology is proposed that assesses these in terms of Key Performance Indicators (KPIs). As a case study, the dehydration of butanol to butenes and dibutyl ether is considered over H-ZSM5 and H-Beta catalysts. It is demonstrated that catalysts should be designed for complete conversion and high butene selectivity, as removal of unreacted 1-butanol requires a complex separation due to the thermo-physical properties of the product mixture.

Mitigation of climate change by reducing the anthropogenic CO2 emissions has recently gained huge momentum. A prime pathway to achieve it in the short-medium term lies within capturing CO2 from industries’ flue gases. Post-combustion CO2 capture (PCC) is the most mature methodology for large-scale CO2 capture and closest to market. However, the conventional chemical absorption-based PCC process implies high regeneration energy requirements, constituting the majority of the energy load when adding PCC to the plant from which the CO2 is going to be captured. As such, reducing the overall parasitic load of the conventional process is of interest. Changing the solvent or improving the process flowsheet are two main methodologies to achieve higher energetic efficiency and lower exergy losses. As there are several reviews dealing with novel solvents, this review critically evaluates the progress in improving the conventional PCC process through modifications of process configurations and shadowed by solvent replacements as drop-in solutions. The process configurations were grouped into absorption and stripping enhancement categories. A comparative analysis was conducted between the different available schemes in the literature and interactions between solvents and process configurations were showcased, highlighting the advantages and shortcomings of some of them. The review reveals the need for standardized framework and optimization algorithms using exergo-economic performance indicators for integrated solvent and process synthesis.

In this study, techno-economic analysis of the sustainable production of polylactic acid (PLA) and biodiesel from Food Waste (FW), with a plant capacity of 50 tons/day, was investigated. In addition, FW of four countries (China, India, Brazil, and the USA) with different compositions of water, protein, lipid and carbohydrate were proposed. Each country has different PLA production rates based on carbohydrate and biodiesel production based on fat. In this study, the FW composition of the USA shows better economic feasibility than other countries. The actual minimum selling price is 6.53 (China), 5.35 (India), 4.75 (Brazil), and 4.29 (US) $/kg. The uncertainty of the MSP was analyzed based on various input limits. The sensitivity analysis was conducted based on biodiesel-selling price, PLA-selling price, income tax, and project lifetime on techno-economic analysis parameters, such as ROI, payback period, IRR and NPV were investigated.

Dry weight and footprint minimization of FPSO processing plants designed for a high CO2 oil field was conducted by integrating sensitivity analysis and genetic algorithm-based optimization. An asymptotic behavior between oil production and equipment dry weight or footprint occurred, demonstrating the possibility of achieving near maximum oil production while significantly reducing equipment dry weight and footprint. The flowsheet combining acetate cellulose membranes with MEA showed superior performance, with best equipment dry weight 22% lower and best equipment footprint 26% lower than the best values of a flowsheet solely relying on acetate cellulose membranes. A flowsheet solely relying on MEA presented the best equipment footprint and dry weight, but with heating demand much higher than the flowsheets that adopt cellulose acetate membranes. The developed process synthesis and optimization tool is efficient, powerful and can be expanded to cover other production systems and other attributes.

International audience; Process synthesis methods enable the determination of unit operations and their interconnection into a process owsheet, with associated design and operating parameters, and responding to given objectives. Modern methods are optimization-based, using for example Mixed Integer Non-Linear Programming (MINLP) formulation to optimize a process superstructure. Finding an adequate denition of the search space is a non-trivial problem in such approaches, especially when the number of possible combinations is high due to the process complexity, and is mostly driven by expertise (e.g. heuristics). Consequently, an inductive bias is intrinsically introduced due to restriction of a limited search space, such as the choice of a superstructure representing a limited set of process alternatives. In this work, an evolutionary method is proposed to generate several process architectures based on a set of available unit operations (and associated models) as elementary building blocks. The procedure is here called ab-initio process synthesis since it does not require any pre-dened process structure. The developed method relies on the use of an Evolutionary Programming (EP), mimicking natural evolution at species-level, for the automatic construction of a process by using mutation operators to choose, assemble and connect elementary building blocks (i.e. unit operations). A Non-Linear Programming (NLP) is used for process evaluation, by simultaneously solving balances and optimizing process degrees of freedom. The method is implemented in a newly developed tool called PSEvo (Process Synthesis by Evolution). An application to a typical reaction-separation problem is presented, using various problem denitions and evolution control parameters, which demonstrates the method capability to generate optimal processes. The possible uses and the challenges of ab-initio process synthesis are nally discussed.

Finding and optimising of synthesis processes for active pharmaceutical ingredients (API) is time consuming. In the finding phase, established methods for synthesis, purification and formulation are used to achieve a high purity API for biological studies. For promising API candidates, this is followed by pre-clinical and clinical studies requiring sufficient quantities of the active component. Ideally, these should be produced with a process representative for a later production process and suitable for scaling to production capacity. This work presents an overview of different approaches for process synthesis based on an existing lab protocol. This is demonstrated for the production of the model drug 4,5,6,7-tetrabromo-2-(1H-imidazol-2-yl) isoindolin-1,3-dione (TBID). Early batch synthesis and purification procedures typically suffer from low and fluctuating yields and purities due to poor process control. In a first step the literature synthesis and purification procedure was modified and optimized using solubility measurements, targeting easier and safer processing for consecutive studies.

Energy is the driving force for growth and per-capita energy consumption is a key parameter for the development of a nation. Carbon dioxide emissions from burning of fossil fuels are the main concerns for global warming. This paper presents a brief review of the available energy sources, CO2-emissions and management, and sustainable chemical processing, where energy consumption, CO2-emission, as well as economics and environmental impacts are considered. Not all available energy sources have been utilized efficiently, while, the energy source causing the largest emission of CO2 is being used in the largest amount. The CO2 management is therefore looking at “curing” the problem rather than “preventing” it. Examples highlighting the synthesis, design and analysis of sustainable chemical processing are presented in the areas of utilization of biomass-based energy-chemicals production, carbon-capture and utilization to produce value added chemicals, and retrofit design of energy intensive chemical processes with significant reduction of energy-consumption are presented. These examples highlight issues of energy-sustainable design, energy-CO2 neutral design, energy-retrofit design, and energy-process intensification. Finally, some perspectives on the status and future directions of carbon dioxide management are outlined.

Summary In this paper, we study single-step natural gas conversion technologies that directly convert methane to olefins and higher hydrocarbons. Despite the relative simplicity of these technologies, the development of processes based on these approaches remains challenging. Accordingly, we utilize process synthesis and modeling to assess the economic feasibility of direct nonoxidative methane conversion strategies. We develop a flexible approach that allows for the systematic evaluation of various technology alternatives and for the identification of the key technology gaps that must be overcome. The results of our analyses demonstrate that an economically feasible direct methane conversion process is contingent upon fundamental research advances in the area of catalytic conversion to increase methane conversion to hydrocarbon products (e.g., coke formation less than 20% and a minimum conversion to products of 25%). Upon this development, further efforts can be devoted to improve ethylene selectivity as well as reduce catalyst cost and overall capital costs.

In this paper, we have designed and synthesized HBT-fused aryl-imidazole derivatives HBTA, HBTB, HBTC and HBTD through π-expanded system, which have large absorption extinction coefficients, relatively high quantum yields, large Stokes shifts. The dyes HBTB, HBTC and HBTD still retained excited state intramolecular proton-transfer property (E* and K* emission). They also exhibited good ESIPT properties in serum. The experimental results and theoretical calculations verified that the nonplanar and nitrogen heterocyclic structure of π-expanded rings was beneficial for the k* emission under the same conditions. Moreover, The HBTA, HBTB, HBTC and HBTD were successfully applied for the imaging in living cells and zebrafish.

We propose a generalized superstructure-based distillation network synthesis model with improved modeling capabilities resulting in an extended solution space. The model can assign multiple mixtures to be separated (e.g., reactor network effluents) to different columns of the network while considering the interactions among different separation steps. In terms of outlets, products with general specifications, including pure components and multi-component mixtures, as well as streams without strict specifications (e.g., reactor recycle streams) can be readily handled. Furthermore, stream bypass is considered to avoid unnecessary separations, and thermal coupling is considered to yield novel solutions. The proposed model enables seamless integration with reactor network synthesis models thereby leading to solutions that are superior to the ones obtained by conventional sequential approaches.

In this paper, a systematic framework for novel and sustainable synthesis-design of processing routes is presented along with the associated computer-aided methods and tools. In Stage 1, superstructure optimization is used to determine the optimal processing route(s). In Stage 2, the design issues are resolved and targets for improvement are identified through the use of integrated tools. In Stage 3, new alternatives are generated using the selected route and the previously identified targets. In addition to the various computer-aided tools, two special tools are presented: (1) a database employing a specially developed knowledge representation system, and (2) Super-O, a software interface that guides users through the formulation and solution of synthesis problems. Super-O transfers data between the different tools, including a library of generic models, representing a wide range of processing options. Application of the synthesis and design stages is highlighted through two case studies (biorefinery and carbon capture-utilization).

The rotating packed bed (RPB), a novel apparatus involving process intensification (PI), has been successfully adopted in the chemical industry in recent decades. This study demonstrates a superstructure synthesis approach for evaluating PI equipment through a case study involving an RPB and a packed bed (PB). A multiscale model links the micro-mechanisms of intensification in the RPB with the macro-level decisions of equipment selection and interconnection. The proposed superstructure considers configurations of an RPB and PB in series, in parallel, and in between, allowing quantification of their synergy. The overall model is formulated as a nonlinear programming problem and is demonstrated with the case of H2S removal from refinery tail gas.

Process flowsheet synthesis is one critical step in the design of any chemical process and is a key factor for consideration in plant operation and economic efficiencies. Previously an automated process flowsheet synthesis is outlined that using a systematic computational approach. This method considers the design and operation variables to converge to an optimum flowsheet using genetic algorithm (GA). In this study, this algorithm is implemented to investigate the potential for a hybrid process combining membrane and cryogenic separation to achieve an efficient and effective carbon dioxide (CO2) capture plant design. The optimum flowsheet is selected using an objective function by screening among a variety of random flowsheets. The studied objective function is a combination of three effective factors in the performance of a flowsheet for this process which are: permeate CO2 purity, CO2 recovery of the system, and energy penalty of the configuration. The outcome of the optimization algorithm is presented as a structurally and parametrically optimized process flowsheet. Within the defined constraints the algorithm estimates that the optimum configuration of a hybrid structure of the membrane/cryogenic system for a three membrane unit process can provide CO2 permeate purity of 0.941, CO2 recovery of 0.979 and energy penalty of 1.249 GJ/t. While the same values for a membrane-based CO2 capture system are 0.940, 0.865 and 3.722 for the optimum estimated flowsheet. The presented results for the optimum hybrid process in comparison with other options shows a promising possibility of exchanging conventional technologies with the proposed optimal configuration. These results along with a global optimality analysis demonstrate the value of automated process synthesis in developing optimal process flowsheet designs.

In the present work, two new, (multi-)parametric programming (mp-P)-inspired algorithms for the solution of mixed-integer nonlinear programming (MINLP) problems are developed, with their main focus being on process synthesis problems. The algorithms are developed for the special case in which the nonlinearities arise because of logarithmic terms, with the first one being developed for the deterministic case, and the second for the parametric case (p-MINLP). The key idea is to formulate and solve the square system of the first-order Karush-Kuhn-Tucker (KKT) conditions in an analytical way, by treating the binary variables and/or uncertain parameters as symbolic parameters. To this effect, symbolic manipulation and solution techniques are employed. In order to demonstrate the applicability and validity of the proposed algorithms, two process synthesis case studies are examined. The corresponding solutions are then validated using state-of-the-art numerical MINLP solvers. For p-MINLP, the solution is given by an optimal solution as an explicit function of the uncertain parameters.

Phenylbenzotriazoles (PBTA) can be generated unintentionally during textile dyeing factories by reduction of dinitrophenylazo dyes and their subsequent chlorination in disinfection process. Eight non-chlorinated PBTAs (non-Cl PBTA) and their related chlorinated PBTAs have been found in rivers and presented mutagenic activity. No data on their aquatic toxicity are available. In this work, two new phenylbenzotriazoles, non-Cl PBTA-9 and PBTA-9, derived from the dye C.I. Disperse Violet 93 (DV93) were synthesized and chemically/toxicologically characterized. Both compounds were more mutagenic than the parental dye in the Salmonella/microsome assay in the presence of metabolic activation (S9). Mutagenicity studies in vivo with mammals would confirm their potential hazard to humans. The two compounds were acutely toxic to Daphnia similis. We developed an analytical method to simultaneously quantify non-Cl PBTA-9, PBTA-9 and DV93 in river waters. Non-Cl PBTA-9 was found in sites under influence of textile effluents but at concentrations that do not pose risk to the aquatic life according to the P-PNEC calculated based on the acute toxicity tests. PBTA-9 was not detected in any samples analyzed. More studies on the aquatic toxicity and water occurrence of PBTAs should be conducted to verify the relevance of this class of compounds as aquatic contaminants.

The synthesis of multiperiod heat exchanger networks (HEN) is a well-studied topic in heat integration. Several methods for identifying heat exchanger network designs that are able to feasibly operate under multiple conditions have been presented. Multiperiod models are certainly a notable form of achieving such resilient designs. In work and heat integration, however, solutions presented so far are for nominal conditions only. This work presents a step-wise optimization-based multiperiod work and heat exchange network synthesis framework. Hybrid meta-heuristic methods are used in the optimization steps. The methodology is able to obtain work and heat exchanger networks (WHENs) that are able to operate under multiple known scenarios. A set of critical conditions for stream properties in work integration is proposed. When these scenarios are modeled as finite operating periods (which are here referred to as non-nominal periods), a WHEN which can feasibly operate under nominal and critical conditions can be obtained. An example is tackled in two cases: the first, with one nominal and six critical, non-nominal periods; the second with two nominal and twelve non-nominal periods. Note that with that number of periods, the problem is considerably more complex than in multiperiod HEN synthesis (which usually comprises three or four periods). Solutions obtained with the present method are compared to those obtaining by simply merging single-period solutions obtained for each period individually. Capital investments are 30.2% and 58.2% lower in Cases 1 and 2 than in straightforwardly merged solutions. The capacity utilization parameters also demonstrate that the overdesign issue is notably reduced in these solutions. The authors gratefully acknowledge the financial support from the Coordination for the Improvement of Higher Education Personnel – Process 88887.360812/2019-00 – CAPES (Brazil) and the National Council for Scientific and Technological Development – Processes 305055/2017-8, 311807/2018-6 and 428650/2018-0 – CNPq (Brazil).

The reactive dividing wall column (RDWC) is a highly integrated process that enables savings in investment and operational costs. Nevertheless, the high grade of integration leads to an extremely complex process behavior, complicating the prediction of the advantageousness of RDWC for a given task during process design. So far, many applications have been proposed in literature for RDWC, but little general knowledge on the influence of reaction system properties on the RDWC has been published. Latter studies are limited to ideal reaction system properties. This article investigates the influence of non-ideal reaction system properties, focusing on azeotropic phase equilibrium. A comprehensive process understanding of how the non-ideal separation properties affect the energy saving potential of RDWC is developed. The results are combined in short heuristics, which allow the process engineer a quick and easy evaluation of the energy saving potential of RDWC for the investigated reaction systems during process synthesis.

This paper introduces a new framework to support synthesis of complex engineering problems using a paradigm that combines optimisation with ontological knowledge modelling. The framework registers and analyzes new solutions by introducing a mechanism of digital certificates to translate structural information and solution features through semantics of an ontology. The solutions are respectively clustered by design features. Tested against complex synthesis of reactor networks, the framework offers a potential to visualize optimization in the course of its development and demonstrates noticeable advantages over conventional methods of a similar basis in convergence and performance.

We propose a superstructure optimization framework for process synthesis with simultaneous heat integration and utility plant design. Processing units in the chemical plant can be modeled using rigorous unit models or surrogate models generated from experimental results or off-line calculations. The utility plant subsystem includes multiple steam types with variable temperature and pressure. For the heat integration subsystem, we consider variable heat loads of process streams as well as variable intervals for the utilities. To enhance the solution of the resulting mixed-integer nonlinear programming models, we develop (1) new methods for the calculation of steam properties, (2) algorithms for variable bound calculation, and (3) systematic methods for the generation of redundant constraints. The applicability of our framework is illustrated through a biofuel case study which includes a novel non-enzymatic hydrolysis technology and new separation technologies, both of which are modeled based on experimental results.

The P-graph (process graph) framework is an effective tool for process-network synthesis (PNS). Here we extended it to multi-period operations. The efficacy of the P-graph methodology has been demonstrated by numerous applications. The unambiguous representation of processes and the availability of the axioms defining combinatorially feasible structures facilitates the development of efficient algorithms to determine maximal structures, solution structures, and optimal structure for processes. However, it is presumed that single-period operation prevails. It implies that the operating conditions and the load of each operating unit remain unchanged, i.e., steady-state operation. This is usually true for the chemical industry but often not in agriculture or where seasonal effects are important. The current work proposes a multi-period operation wherein the load of operating units varies from period to period to accommodate demand, assuming operating conditions remain steady in each period. A modeling technique is proposed to represent operating units in the multi-period operation. The different periods are connected by “fictitious” streams, which ensures, that the unit is sized properly.

Aiming for more energetically efficient and sustainable solutions, academic attention to work and heat integration (WHI) has grown in the last decade. Simultaneous models for work and heat exchanger network (WHEN) synthesis often derive from heat integration (HI) frameworks. However, it can be noted that simultaneous optimization models for WHI are considerably more complex to solve than in the HI case. The design of efficient pressure manipulation routes (i.e., allocation and sizing of compression and expansion machinery) in process streams prior to heat exchange match allocation can make the optimization procedure more efficient. This work proposes a systematic procedure based on a model that employs Pinch Analysis concepts for defining these routes based on capital and operating cost targets. The solution approach is a hybrid meta-heuristic method based on Simulated Annealing (SA) and Particle Swarm Optimization (PSO). The obtained routes are then converted into a HI problem by fixing pressure manipulation unit sizes. The detailed HI solution is finally transferred into a WHI optimization model as initial design. In the two tackled examples, the total annual costs (TAC) predicted by the Pinch-based model differed by 0.5% and 1.2% from the final optimized WHEN obtained in the detailed WHI framework. The authors gratefully acknowledge the financial support from the Coordination for the Improvement of Higher Education Personnel – Processes 88887.360812/2019–00 and 88881.171419/2018–01 – CAPES (Brazil) and the National Council for Scientific and Technological Development – Processes 305055/2017–8, 428650/2018–0 and 311807/2018–6 – CNPq (Brazil).

Reactive distillation (RD) is a useful process intensification technique used in the chemical process industries as it offers important advantages such as energy and cost savings, relative to conventional technologies. However, industrial application of RD is still limited by the complexity of designing and understanding such a complex process. While simple, robust shortcut design methods that require only basic information (such as the relative volatility ofcomponents) exist for conventional distillation, such methods for evaluating the applicability of RD are not yet established. This work fills this gap by presenting a new systematic framework for assessing the RD applicability based on a mapping method. The method enables RD designs to be screenedusing only relative volatilities and chemical equilibrium constant as input data. The evaluation focuses on reactions involving four components (A + B ⇌ C + D) with various boiling point orders, which are of most industrial importnace. The proposed systematic framework is validated through its application to five case studies, (trans-)esterifications presenting various separation challenges due to the formation of azeotropes. This novel approach offers a valuable aid for engineers in taking an educated go/no-go decision in the very initial stages ofconceptual design, before performing any rigorous simulations of RD flowsheets.

We propose a general framework for the formulation of superstructure-based optimization models for holistic process synthesis. First, we redefine the fundamental problems of reactor, separation, and heat exchanger network synthesis, by presenting generalized problem statements, to make them amenable to seamless integration with each other. Second, we describe the general forms of models that can be developed to address these generalized problems and identify some key characteristics. Notably, for each system, we identify internal variables used only within the system, cost variables used in the objective function, and, importantly, coupling variables for the coupling between systems. Third, we outline some literature models that can be used to address the generalized problems and present new models to couple the three systems. Finally, we show how the individual components (systems and coupling models) can be integrated to formulate a single simultaneous reactor, separation, and heat exchanger network synthesis.

In this paper, we establish a connection between process integration and process intensification. Focusing on processes with material recycle, we use an asymptotic analysis to demonstrate that intensification represents a limit case of tight integration through significant material recycling. Based on this result, we propose a novel avenue for discovering intensification opportunities at the process design stage. Subsequently, we investigate the dynamics and control implications of the transition from process integration to process intensification. We demonstrate that, for the same steady-state performance, the dynamic response of an integrated process is slower than that of its intensified equivalent. Also, we provide a theoretical justification for existing empirical arguments concerning the loss of control degrees of freedom caused by process intensification. The theoretical developments are applied on a reaction–separation–recycle process example.

This paper gives the author's perspective on some of the open questions and opportunities in process synthesis focusing on separation systems as the application. Driven by energy and environmental concerns and challenged by introduction of new raw materials, this author anticipates significant advances in: (1) novel approaches that integrate experimental studies and process synthesis activities, and multi-scale and surrogate models for accurately capturing the behavior of these unconventional mixtures, (2) systematic generation of alternatives for processing these mixtures, and (3) global, robust, and stochastic optimization for identifying the optimum alternative. This paper is an extended version of a conference paper ( Cremaschi, 2014 ) presented at the 8th International Conference on Foundations of Computer-Aided Process Design.

This paper describes the development and application of a process-group contribution method to model, simulate and synthesize chemical processes. Process flowsheets are generated in the same way as atoms or groups of atoms are combined to form molecules in computer aided molecular design (CAMD) techniques. The fundamental pillars of this framework are the definition and use of functional process-groups (building blocks) representing a wide range of process operations, flowsheet connectivity rules to join the process-groups to generate all the feasible flowsheet alternatives and flowsheet property models like energy consumption, atom efficiency, environmental impact to evaluate the performance of the generated alternatives. In this way, a list of feasible flowsheets are quickly generated, screened and selected for further analysis. Since the flowsheet is synthesized and the operations in the flowsheet designed through predictive models to match a set of design targets, optimal solution of a given synthesis problem is guaranteed.

In Urselmann et al., 2011a , Urselmann et al., 2011b we presented a memetic algorithm (MA) for the design optimization of reactive distillation columns. The MA is a combination of a problem-specific evolutionary algorithm (EA) that optimizes the design variables and a mathematical programming (MP) method that solves the continuous sub-problems with fixed discrete decisions which are proposed by the EA to local optimality. In comparison to the usual superstructure formulation, the search space of the MA is significantly reduced without excluding feasible solutions. The algorithm computes many different local optima and can handle structural restrictions and discontinuous cost functions. In this contribution, a systematic procedure to modify the MA to solve more complex design problems is described and demonstrated using the example of a reactive distillation column with an optional side- or pre-reactor with structural restrictions on the number of streams. New concepts to handle connected and optional unit operations are proposed.

In this paper a multi-level, multi-scale framework for process synthesis-intensification that aims to make the process more sustainable than a base-case, which may represent a new process or an existing process, is presented. At the first level (operation-scale) a conceptual base case design is synthesized through the sequencing of unit operations and subsequently analyzed for identifying process hot-spots using economic, life cycle and sustainability metrics. These hot-spots are limitations/bottlenecks associated with tasks that may be targeted for overall process improvement. At the second level (task-scale) a task-based synthesis method is applied where one or more tasks representing unit operations are identified and analyzed in terms of means-ends for generating intensified flowsheet alternatives. At the third level (phenomena-scale) a phenomena-based synthesis method is applied, where the involved phenomena in various tasks are identified, manipulated and recombined to generate new and/or existing unit operations configured into flowsheet alternatives that target the tasks associated with hot-spots. Every lower-scale or higher-level, generates more alternatives than their corresponding larger-scale. Those alternatives that are able to address the identified hot-spots therefore give innovative and more sustainable process designs that otherwise could not be found from the larger-scales. In this paper, membrane-based operations identified through this framework are highlighted in terms of extension of the combined intensification-synthesis method and its application to generate membrane-based operations. Also, application of the framework is illustrated through a case study involving the production of methyl acetate where membrane-based intensified operations play a major role in determining more sustainable process design alternatives.

The careful development and use of integrated models for process development may provide detailed process understanding, especially of unit operations that are far apart in a bioprocess train. In addition, such models may facilitate rapid, rational process development, and thus reduce development costs for new medicines. This review considers models for multiple unit-operations in sequence, and where applicable the use of single models to describe multiple phenomena. Since the bioprocess literature in this area is limited, relevant work from other areas is also reviewed. Both data-based and mechanistic models are discussed, as are their applications to process synthesis, optimisation, control and monitoring.

High accuracy models can be obtained by using different types of surrogate models that accurately approximate equipment phenomenological models and can be used in synthesis problems, leading to faster and more precise solutions. Two types of surrogate models are used to approximate equipment phenomenological models: polynomial and neural network-based. In some cases, these surrogate models are not able to represent more complex equipment. An original methodology to reformulate these models using equations from shortcut equipment design is proposed. A medium-size case study involving fifteen units is presented. The synthesis problem is solved in a short computational time, leading many local solutions. Since several local optima objective function values are very close to each other, the choice of the best configuration among those found should be done qualitatively, because the differences among the objective function values are not significant if compared to the accuracy of equipment cost correlations in the literature.

The fast development of new products, process technologies and production methods has made the decision making in process design and synthesis a complicated task. Combination of all feasible process pathways and solutions creates a huge super structure. Thus, to determine the optimum solution and flexible alternative solution is a big challenge for Process Network Synthesis (PNS). Process graph (P-graph) approach has been extended gradually to provide a friendly and fast optimum result for PNS problem. This paper overviews the application of P-graph in new PNS areas in the aspect of synthesis, optimisation, planning and management. The paper demonstrates the extension of P-graph via several case studies such as effective supply chain systems, carbon emission reduction systems and cleaner production process synthesis. The paper also highlights the advantages of P-graph in process network synthesis.

Process synthesis, modeling, and experiments are the major components in designing industrial wastewater treatment plants. As industrial wastewater contains process-specific pollutants and is highly varied in nature, limited information is available in selecting the best treatment unit for a given industrial wastewater by heuristics. Experiments can complement the heuristics-based flowsheet development by determining the best treatment unit for the industrial wastewater and obtaining model parameters for a more reliable process simulation. This finally leads to a high performance and cost effective industrial wastewater treatment plant.

ZnO–SnO 2 branch–stem nanostructures were realized on a basis of a two-step process. In step 1, SnO 2 -stem nanowires were synthesized. In step 2, ZnO-branch nanowires were successfully grown on the SnO 2 -stem nanowires through a simple evaporation technique. We have pre-deposited thin Au layers on the surface of SnO 2 nanowire stems and subsequently evaporated Zn powders on the nanowires. The ZnO branches, which sprouted from the SnO 2 stems, had diameters in a range of 30–35 nm. As-synthesized branches were of single crystalline hexagonal ZnO structures. Since the branch tips were comprised of Au-containing nanoparticles, the Au-catalyzed vapor–liquid–solid growth mechanism was more likely to control the growth process of the ZnO branches. To test a potential use of ZnO–SnO 2 branch–stem nanostructures in chemical gas sensors, their sensing performances with respect to NO 2 gas were investigated, showing the promising potential in chemical gas sensors.

Concerns about diminishing petroleum reserves, enhanced worldwide demand for fuels and fluctuations in the global oil market, together with climate change and national security have promoted many initiatives for exploring alternative, non-petroleum based processes. Among these initiatives, biorefinery processes for converting biomass derived carbohydrates into transportation fuels and chemicals are now gaining more and more attention from both academia and industry. Process synthesis, which has played a vital role for the development, design and operation of (petro) chemical processes, can be predicted to play a significant role in the design and commercialization of sustainable and cost-effective biorefinery processes. The main objective of this perspective paper is to elucidate the potential opportunities that biorenewables processing offers to optimal synthesis; challenges and future directions in this field are also concisely discussed. An attempt is made with this perspective to stimulate more and more efforts to optimally synthesize and design biorenewable conversion process to accelerate the commercialization of the biorefinery technology and further reduce the heavily reliance on petroleum-derive fuels.

Biodiesel is an attractive biofuel because it can be used directly with the traditional petro diesel engines, either as a substitute or as a blending component. There are several alternatives that can be used to produce biodiesel. In this work, we developed a superstructure optimization model to synthesize the optimum biodiesel production plant, i.e., the one that gives the minimum net present sink. To reduce the computational cost of solving the resulting disjunctive programming, the surrogate models utilizing artificial neural networks (ANNs), have been developed to replace the unit operation, thermodynamics and mixing models. The optimum solution with the alkali-catalyzed reactor (obtained in five CPU seconds) has a total net present sink of about $41 million, which differs less than one percent from the result obtained by modeling the solution in a process simulator. However, this level of accuracy required a large amount of data to train the ANNs.