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Definition: This Process Synthesis and Intensification (PS+I) framework uses a low-aggregation superstructure to solve the synthesis problem, and its solution is interpreted and translated into a task-integrated intensified process. Then, the process is post-optimized to find a better balance between operation and equipment costs. The solution leads to novel and counterintuitive intensified processes with low cost and energy requirements. [ABSTRACT FROM AUTHOR]

This Process Synthesis and Intensification (PS+I) framework uses a low-aggregation superstructure to solve the synthesis problem, and its solution is interpreted and translated into a task-integrated intensified process. Then, the process is post-optimized to find a better balance between operation and equipment costs. The solution leads to novel and counterintuitive intensified processes with low cost and energy requirements.

The study presents ten different exercises covering various computational tools. These exercises are practical applications presented to improve the understanding and skills of students in important concepts of chemical-aided process synthesis. A few exercises aim to build a foundation in computational techniques for chemical engineering undergraduates. The exercises are based on a spreadsheet that covers the design of regression analysis to find the optimum Antoine constants, array calculation for multicomponent distillation material balance, and the generation of a Gantt chart to plan and study the activities of batch processes. The other exercises included an introduction to process simulation, simulation, and reactor rating, and a simulation of multicomponent shortcut distillation. These exercises provide students with hands-on experience in utilizing process simulation software essential for analysing and optimizing chemical processes in real-world scenarios. The exercises also included the design of a heat exchanger network and solving a linear programming problem. An anonymous survey was collected from the cohort that had undergone the exercises, and the practical grades were compared with the batch that did not study the proposed exercises. Additionally, student feedback on practical exercises was collected. Based on the experience of the course coordinator and the collected feedback from participants, it was clear that the exercises helped students to inculcate critical thinking and self-learning abilities. An article essentially sheds light on the computer-aided practical exercises that enable chemical engineering graduates to engage in lifelong learning. [Display omitted] • Ten exercises cover spreadsheet, regression, array, Gantt charts, and more. • Diverse computer-aided exercises promote lifelong learning. • Coordinator experience and feedback highlight self-learning capability. • An integrative approach that combines engaging practical exercises enhances the application of theory. • Computer-aided practical exercises for process synthesis actively engage students. [ABSTRACT FROM AUTHOR]

Reactive distillation (RD) is a process intensification technique that offers major advantages over conventional technologies by enabling the integration of reaction and separation into a single apparatus. This integration introduces and exploits complex interaction between mass transfer, chemical reaction and hydrodynamics within an RD column; however, these complexities can hinder the adoption of RD by industry. Many approaches have been developed by the scientific community to advance understanding and to expedite the initialization of RD design at the conceptual level. This paper critically discusses recent developments in conceptual methods for synthesizing RD processes. This review paper is the first to consider the range of available approaches for assessing the technical feasibility, controllability, economic viability, and sustainability of RD units by taking into account various configurations in which RD is treated as a new unit operation, and as part of process synthesis in a different way of designing processes based on functions. The review also addresses complex configurations, such as advanced RD technologies. Special attention is paid to process modeling and simulation as well as to education. Knowledge gaps to be filled by further research are indicated. © 2024 The Authors. Journal of Chemical Technology and Biotechnology published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry (SCI). [ABSTRACT FROM AUTHOR]

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

One of the most important contributions of Japanese researchers is the proposition of the superstructure concept. Umeda and coworkers proposed in 1972 a system structure for a reaction-separation process structure in which split ratios determine the connecting flows. Later, this concept changed to structure parameter and, finally, to the superstructure. Since 1980, superstructure-based optimization has been a very active field in Process Systems Engineering (PSE). This review presents the research done in Japan over the last 50 years as a tribute to the pioneering work of Japanese engineers and researchers in Academia and Industry. Recent advancements in superstructure-based optimization are in the direction of low aggregation representations that contain functional modules covering phenomenological aspects. The synthesis of distillation structures, biomass utilization, supply chain networks, and (bio)pharmaceutical processes are active topics in Japan, and further extensions and applications are expected in the future.

The best structure of multicomponent separation techniques can be obtained using optimal distillation sequencing. Because distillation sequences contribute significantly to the fixed and operational cost of the entire chemical process, developing a systematic approach for choosing the most appropriate and economic distillation sequences becomes an important field of study. Due to its high dimensional space and combinatorial nature, synthesis of the optimal conventional distillation column sequence is a tough problem in the field of process plant development and optimization. A novel method for the synthesis of an optimal conventional distillation column sequence is suggested in this study. Genetic algorithm, an evolutionary algorithm is at the heart of the proposed method. The Total Annual Cost (TAC) is the main basis used to evaluate alternative configurations. To estimate the total cost of each sequence, rigorous methods are used to design all columns in the sequence. The proposed method's performance and that of the conventional quantitative approach are compared using the results of a five component benchmark test problem used by researchers in this field. According to the comparison results, the suggested algorithm outclasses the other methods and is more adaptable than other existing approaches. [ABSTRACT FROM AUTHOR]

Advanced reactive distillation technologies (ARDT) are often overlooked during process synthesis due to their complexity. This work proposes the use of operating windows with additional features to identify suitable operating limits for ARDT. Data needed to construct the operating windows are thermodynamic properties, kinetic parameters, constraints of materials and experimental methods, and heuristics. In addition, two new concepts are proposed to represent complex features: representative components and a sliding window. Results include the identification of suitable operating limits for ARDT to help assess their feasibility early in process design. The proposed approach is demonstrated by case studies. Methyl acetate production can be carried out at low pressures (0.5–3.6 atm), while lactic acid purification requires vacuum conditions (0.3–0.8 atm) to avoid thermal degradation. Tert-amyl methyl ether production was evaluated in two scenarios where the effect of side reactions is evidenced in a reduction of the reaction window due temperature limits to favour the main reaction over side reaction. This study is the first to evaluate advanced reactive distillation technologies using a graphical representation in an operating window to aid process synthesis, where the results provide key selection insights. • Process intensification enabled via advanced reactive distillation technologies. • Operating windows construction to expand the applicability of reactive distillation. • Simplification strategies to represent multicomponent systems in operating windows. [ABSTRACT FROM AUTHOR]

Process synthesis experiences a disruptive transformation accelerated by artificial intelligence. We propose a reinforcement learning algorithm for chemical process design based on a state‐of‐the‐art actor‐critic logic. Our proposed algorithm represents chemical processes as graphs and uses graph convolutional neural networks to learn from process graphs. In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions. We implement a hierarchical and hybrid decision‐making process to generate flowsheets, where unit operations are placed iteratively as discrete decisions and corresponding design variables are selected as continuous decisions. We demonstrate the potential of our method to design economically viable flowsheets in an illustrative case study comprising equilibrium reactions, azeotropic separation, and recycles. The results show quick learning in discrete, continuous, and hybrid action spaces. The method is predestined to include large action‐state spaces and an interface to process simulators in future research. [ABSTRACT FROM AUTHOR]

Cavitation extraction, water/hexane washing, and cavitation precipitation-based process synthesis and optimization were performed for the isolation and purification of biomass-derived paclitaxel. The final purity, overall yield, and operating time were significantly improved when negative pressure cavitation extraction was followed by sequential washing with water and hexane, adsorbent treatment, and negative pressure cavitation fractional precipitation using acetone-pentane and methanol-water. This procedure showed a purity and yield increase of 11.0–45.6% and 2.2–29.6%, respectively, and an operating time reduction of 56.2–98.3% compared to the traditional methods. [ABSTRACT FROM AUTHOR]

In order to reach product purity specifications, reactor outlet streams are often purified using distillation columns, and the unreacted feed is recycled to the reactor. This makes Reaction-Separation processes very important in chemical industries. In the current study, a new version of the Reaction/Distillation Matrix algorithm previously presented by the authors is presented. The current version of the algorithm is capable of generating Reactive Distillation configurations as attractive process alternatives and also recycle streams to prevent the loss of unreacted feed components. Besides, major modifications have been carried out to the algorithm to make it more efficient and user-friendly. Production of cumene and dimethyl ether were studied as case studies to evaluate the performance of the developed algorithm. The conventional sequences generated using the Reaction/Distillation Matrix algorithm were simulated by Aspen Plus and then optimized using the Genetic Algorithm. It is observed that cost of reactive distillation configuration was approximately 62 % and 10 % lower than for the conventional process in the cumene production and dimethyl ether production case studies, respectively. • A new version of the Reaction/Distillation Matrix algorithm for the synthesis of reaction-separation sequences is presented. • The new version is capable of generating Reactive distillation configurations along with conventional sequences. • Recycle streams as an essential part of the Reaction-Separation-Recycle processes are now considered in the algorithm. • The performance of the presented algorithm is evaluated using two case studies of cumene and DME production. [ABSTRACT FROM AUTHOR]

The synthesis of property-based total water networks with multiple interceptors is investigated by using operator potential concepts. Two kinds of network configurations are considered according to whether process sources are allowed to mix at inlet of the interceptors. For a non-mixing network, each interceptor treating one process source, the synthesis procedure is simple. However, too many interceptors might be needed and the corresponding costs will be high for the systems with a large number of process sources. For a mixing network, process sources are allowed to mix at inlet of the interceptors, the network construction can be simplified and the total annual costs can be reduced. An iteration procedure is proposed to design mixing-configuration networks. In the synthesis procedure, the values of operator potential of demands are used to determine the sequence of demands to be satisfied, the values of operator potential of sources are used to select the process sources to be treated. A linear programming approach is adopted to allocate the sources to each demand. The results of four examples obtained with the proposed methods show that the networks with lower fresh source consumption, or simpler structure, or lower total annual cost can be obtained compared to the literature results. In addition, the proposed methods have the features of simple calculation and clear engineering meaning. [Display omitted] • Property-based total water networks with multiple interceptors are synthesized. • Operator potential concepts are used to guide the synthesis procedure. • Networks with both non-mixing and mixing configurations for sources are considered. • The proposed method has merits of simple calculation and clear engineering meaning. • Networks with lower fresh source consumption and/or total annual cost are obtained. [ABSTRACT FROM AUTHOR]

In this paper, a feasible path-based branch and bound (B&B) algorithm is proposed to solve mixed-integer nonlinear programming problems with highly nonconvex nature through integration of the previously proposed hybrid feasible-path optimisation algorithm and the branch and bound method. The main advantage of this novel algorithm is that our previously proposed hybrid steady-state and time-relaxation-based optimisation algorithm is employed to solve a nonlinear programming (NLP) subproblem at each node during B&B. The solution from a parent node in B&B is used to initialize the NLP subproblems at the child nodes to improve computational efficiency. This approach allows circumventing complex initialisation procedure and overcoming difficulties in convergence of process simulation. The capability of the proposed algorithm is illustrated by several process synthesis and intensification problems using rigorous models.

Heat Exchanger Network (HEN) synthesis is primarily formulated as a mixed integer non-linear programming (MINLP) problem based on method of stage-wise superstructure (SWS). Approaches to obtain an optimal HEN configuration can adopt deterministic algorithms. But, as a large scale problem, it is difficult to solve due to the complexities arisen from nonlinearities of SWS. To overcome the model nonlinearities, stochastic algorithms and meta-heuristic approaches have been proposed in the literature to tackle the problem. However, it reaches a near-optimal HEN configuration from a series of stochastic solutions, which are obtained by the execution of many computational procedures with extensive time resource, and a global optimum will not be guaranteed from only randomly generated results. In this paper, an enhanced SWS approach is proposed, in which new temperature and heat duty constraints are updated to reduce the redundant combinations and avoid conflicted calculation of non-isothermal mixing energy balance. The present model is also extended to allow flexible stream splitting for practical applications. Then, a deterministic-based global solver (GAMS/BARON) is applied in solving three case studies. The results show that the proposed approach can provide cost-efficient HEN solutions with lower TAC than using existing stochastic and deterministic algorithms.

The design and engineering of piping and instrumentation diagrams (P&ID) is a very time-consuming and labor-intensive process. Although P&IDs show common patterns that could be reused during development, the drawing is usually created manually and built up from scratch for each process. The aim of this paper is to recognize these patterns with the help of artificial intelligence (AI) and to make them available for the development and the drawing process of P&IDs. In order to achieve this, P&ID data is made accessible for AI applications through the DEXPI format, which is a machine-readable, manufacturer-independent exchange standard for P&IDs. It is demonstrated how deep learning models trained with DEXPI P&ID data can support the engineering as well as drawing of P&IDs and therefore decrease labor time and costs. This is achieved by assisted prediction of equipment in P&IDs based on recurrent neural networks as well as consistency checks based on graph neural networks.

We propose the integration of simplified graphically inspired feasibility constraints into optimization‐based models for distillation network synthesis. The approach facilitates the use of surrogate, potentially data‐based, distillation column models while considering feasibility in a computationally efficient manner. The proposed approach can aid the formulation of efficient approaches for preliminary distillation network synthesis. [ABSTRACT FROM AUTHOR]

Large‐scale strongly nonlinear and nonconvex mixed‐integer nonlinear programming (MINLP) models frequently appear in optimization‐based process synthesis, integration, intensification, and process control. However, they are usually difficult to solve by existing algorithms within acceptable time. In this study, we propose two robust homotopy continuation enhanced branch and bound (HCBB) algorithms (denoted as HCBB‐FP and HCBB‐RB) where the homotopy continuation method is employed to gradually approach the optimum of the NLP subproblem at a node from the solution at its parent node. A variable step length is adapted to effectively balance feasibility and computational efficiency. The computational results from solving four existing process synthesis problems demonstrate that the proposed HCBB algorithms can find the same optimal solution from different initial points, while the existing MINLP algorithms fail or find much worse solutions. In addition, HCBB‐RB is superior to HCBB‐FP due to much lower computational effort required for the same locally optimal solution. [ABSTRACT FROM AUTHOR]

Waste tyre generation in South Africa is one of the issues that have not found a sustainable solution. Approximately 1.5 billion tyres are produced annually around the world, and South Africa contributes about 11 million tyres accounting to about 177 835 tons of waste annually. Numerous technologies have been used as possible pathways to recycle waste tyres. This review analyzes the main advances in waste tyre pyrolysis and gasification technologies and lists challenges and successes. For waste tyre pyrolysis, recovering high-value products such as limonene, benzene, xylene, and activated carbon can make the process profitable. For gasification, the objective of new studies is for the producer gas to have a desirable composition for further application in the production of chemicals, heat and power and synthesis of liquid fuels. Also, more research is required to fill the gap in the optimum conditions for solar integrated pyrolysis and gasification of waste tyres. A comprehensive comparative techno-economic analysis of chemicals production from waste tyre via gasification is required to determine the most commercially viable route for tyres. [ABSTRACT FROM AUTHOR]

Many process synthesis and design problems in engineering are actually mixed integer nonlinear programming problems (MINLP), because they contain both continuous and integer variables. These problems are generally recognized to be complex and intractable by virtue of the combinatorial characteristic. In order to effectively solve process synthesis and design problems, a global particle swarm optimization (GPSO) algorithm is proposed in this paper. GPSO algorithm makes two improvements on original particle swarm optimization (PSO) algorithm: first, it introduces a global inertia weight, which is beneficial for improving its global searching capacity during the whole optimization process; second, it adopts a mutation operation with a small probability, which enables the GPSO algorithm to get rid of the local optimum easily. Simulation results show that the GPSO algorithm has high efficiency on finding the optimal solutions, and it has stronger convergence than the other four particle swarm optimization algorithms.

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.

In this work, we synthesized and economically analyzed a sustainable process of industrial production of microencapsulated anthocyanins powder (MAP) from Hibiscus sabdafiffa L. calyx (HSC) with the assistance of SuperPro Designer software. Open literature data were used in the synthesis of flowsheet and minimum selling price (MSP) of MAP was the only economic parameter used to assess the profitability of MAP production plant. Scale-up studies in the range of 0.127–130.393 × 103 kg/y MAP were conducted to identify the most economically feasible production capacity of MAP. Sensitivity and uncertainty analyses were further conducted on the most economically feasible capacity to determine technical and cost variables of significance. Material and energy demands and cost parameters increased with pant capacity. A power law relationship (R2 = 0.9081) was obtained between the MSP and selected range of plant capacities. The plant capacity of 108.551 × 103 kg/y MAP had the least MSP of 4013 US$/kg MAP and was selected as the most economically feasible capacity. Regarding the economics of plant capacity of 108.551 × 103 kg/y MAP, Packed Bed Adsorption (PBA) chromatography column was required in multiples and its cost contributed approximately 59% to the total equipment purchased cost (EPC). Consumables cost was the highest contributor to total operating cost (TOpC) and MSP showed a linear dependence (R2 = 0.9935) on the discount rate. Sensitivity and uncertainty analyses showed that resin binding capacity, PBA resins purchase cost and Hibiscus sabdafiffa L. calyx anthocyanins (HSCAs) recovery had most contributions to uncertainties in MSP.

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

A new graphical method for the fundamental design of pressure‐swing distillation (PSD) and valid heat integration of the designed process is demonstrated based on a pinch analysis. As the minimum pressure constraint, the pinch pressure is determined by this graphical method to secure the necessary temperature driving force and circumvent the distillation boundaries. For a feasibility evaluation of heat‐integrated PSD (HIPSD), the suggested approach requires visualization of the pinch temperature contours and the feasible composition region of an initial feed according to the pressure change and dynamic properties of ternary azeotropic systems. We thus carried out an in‐depth examination by conducting case studies to show the feasible integration into HIPSD. The new insight is that the HIPSD system can have different pinch pressures depending on the column sequence or the separation order of pure components. In addition, the analysis verifies that energy‐efficient HIPSD can be generated by considering pinch constraints. [ABSTRACT FROM AUTHOR]

Flowsheet optimization is an important part of process design where commercial process simulators are widely used, due to their extensive library of models and ease of use. However, the application of a framework for global flowsheet optimization upon them is computationally expensive. Based on machine learning methods, we added mechanisms for rejection and generation of candidates to a framework for global flowsheet optimization. These extensions halve the amount of time needed for optimization such that the integration of the framework in a workflow for iterative process design becomes applicable. [ABSTRACT FROM AUTHOR]

As part of Industry 4.0, workflows in the process industry are becoming increasingly digitalized. In this context, artificial intelligence (AI) methods are also finding their way into the process development. In this communication, machine learning (ML) algorithms are used to suggest suitable separation units based on simulated process streams. Simulations that have been performed earlier are used as training data and the information is learned by machine learning models implemented in Python. The trained models show good, reliable results and are connected to a process simulator using a.NET framework. For further optimization, a concept for the implementation of user feedback will be assigned. The results will provide the fundamental basis for future AI‐based recommendation systems. [ABSTRACT FROM AUTHOR]

The rubber peptizer 2,2′-dibenzoylaminodiphenyl disulfide is typically synthesized from C7H5NS, NaOH, H2SO4, and H2O2, but these reactants were replaced with C6H4ClNO2, C2H6O, Na2S, S, and N2H4·H2O, and these raw materials effectively improved the synthesis yield, reduced the number of synthetic steps, and made the synthetic process greener. Although the catalyst is difficult to recover, it effectively avoids using ethanol as a volatile organic solvent. The Aspen Plus method was used to simulate the key processes in the synthesis in the experimental conditions as the boundary conditions. The simulation results show that the feed ratio of C7H5NS, H2O2, and C7H5ClO directly determines the yield of the reaction, and the equivalents of NaOH, H2SO4, and Na2CO3 indirectly affect the yield of the reaction by changing the reaction environment and controlling the formation of byproducts. The temperature of the ring-opening reaction and the acylation reaction should be maintained within 110–120°C to maximize the yield. The oxidation reaction temperature also directly affects the reaction yield and should be kept below 40°C. The simulation results are consistent with practical industrial production conditions. Based on the developed green synthesis process and the optimal process parameters obtained from the simulation, the industrial-scale production of 10,000 tons of 2,2′-di benzoyl amino diphenyl disulfide was carried out. Compared with that of o-nitrochlorobenzene, the yield of 2,2′-dibenzoylaminodiphenyl disulfide increased from approximately 72% to more than 90%. Using this method instead of the original synthesis method avoids the use of o-nitrochlorobenzene, which is neurotoxic; Raney nickel as the metal catalyst, which is difficult to recycle with existing environmental protection technologies; and ethanol as the organic solvent, which is associated with environmental problems. The amine tail gas that is easily generated in the original synthesis method is not generated in this system, and the drying step is eliminated.

This article provides a systematic review of recent progress in optimization-based process synthesis. First, we discuss multiscale modeling frameworks featuring targeting approaches, phenomena-based modeling, unit operation-based modeling, and hybrid modeling. Next, we present the expanded scope of process synthesis objectives, highlighting the considerations of sustainability and operability to assure cost-competitive production in an increasingly dynamic market with growing environmental awareness. Then, we review advances in optimization algorithms and tools, including emerging machine learning-and quantum computing-assisted approaches. We conclude by summarizing the advances in and perspectives for process synthesis strategies.

A new synthesis method for reactive distillation processes is proposed. At each stage of a column, vapor–liquid equilibrium (VLE) is assumed and kinetically controlled reaction in liquid phase is considered. First, the liquid composition space is divided into small subspaces. Then, for each subspace a representative liquid composition is decided and assigned to a module corresponding to a stage of a distillation column. Then, after the calculation of the VLE and the reaction rate, the distribution network (superstructure) connecting all modules by vapor and liquid flow paths is constructed. The feature of the proposed model is that all constraints are linear to the optimization variables: the liquid and vapor flow rate and the liquid hold‐up. The developed method was applied to the metathesis reaction of 2‐pentene, and a completely new process structure was obtained. The effectiveness of the implied structure was confirmed through a comparison with conventional structures. [ABSTRACT FROM AUTHOR]

[Display omitted] • A surrogate-based optimization method is proposed for simultaneous synthesis. • An ANN-based surrogate model is established for process unit modeling. • An enhanced MINLP model is introduced to address the variable HEN. • Two examples are studied to illustrate the effectiveness of the proposed method. Heat-integrated process synthesis is fundamental to achieve higher energy efficiency. The well-known sequential-conceptual methods have been widely adopted to solve the synthesis problem in a hierarchical manner. However, the natural hierarchy fails to consider complex interactions between the unit operation and the heat integration. To address this issue, a surrogate-based optimization framework is proposed for simultaneous synthesis of chemical process and heat exchanger network. An artificial neural network (ANN)-based surrogate model, derived from the simulation data generated via rigorous mechanism modelling approach, is established for process units to replace their complex realistic models. With surrogate model formulation incorporated into heat integration, an enhanced transshipment-based mixed integer nonlinear programming model is introduced to synthesize heat exchanger network with variable flowrates and temperatures, aiming at the maximized annual profit. Finally, two example studies are investigated to demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

We address the synthesis of separation systems when multiple technologies are available. Since different technologies exploit different properties, we introduce a component ranking system based on those properties and use it to (1) construct matrices that identify possible separation splits and (2) generate a network-based superstructure that encompasses numerous promising configurations comprising multiple separation technologies. Each separation split is performed in a separation block representing a set of operations enabling sharp separation. The approach adopts a generalized problem statement which allows us to consider the simultaneous synthesis of separation and reactor systems. We present three examples to illustrate the applicability of the approach. • Proposed simultaneous approach to synthesize multi-technology separation systems. • A superstructure is generated using component rankings and separation matrices. • Approach addresses new problems including reactor-separation synthesis. [ABSTRACT FROM AUTHOR]