Shell-and-tube heat exchangers are critical assets in the process industries, yet their design optimization typically relies on deterministic formulations that ignore operational variability. This study presents a systematic comparison of four optimization approaches, spanning three paradigms for decision-making under uncertainty, applied to shell-and-tube heat exchanger design. The objective minimizes total annualized cost (comprising capital and pumping costs) subject to thermal duty, pressure drop, velocity, and geometric constraints. Six uncertain parameters are modeled across three categories: mass flowrates (coefficient of variation = 10%), inlet temperatures (coefficient of variation = 2%), and fouling resistances (uniform distribution). Shell-side heat transfer is computed via the Bell-Delaware method, while tube-side correlations follow Sieder-Tate. The four approaches benchmarked are: (1) deterministic optimization as a baseline, (2) sample-average approximation, a classical... [more]

Sohar Port and Freezone is well-positioned to drive Oman`s economic future through continued expansion, increased reliance on sustainable energy, and the integration of advanced digital tools. Sohar contributes 25% of the current GHG emissions in Oman. Decarbonizing Sohar industrial clusters is a strategic challenge for emerging economies striving to attain net-zero objectives. This research develops a superstructure optimization framework for designing an integrated Carbon Capture, Utilization and Storage (CCUS) value chain, specifically applied to the Sohar Port and Freezone in Oman. A mixed-integer linear programming (MILP) formulation that simultaneously selects capture technologies, identifies optimal transport modes (pipeline, trucking, and shipping), and allocates CO2 flows among utilization and geological storage alternatives. The novelty of this work lies in the multi-scale integration of techno-economic, spatial, and policy dimensions, coupled with GIS-based route generation... [more]

Flowsheet synthesis is an integral step in process design, entailing the selection of a set of unit operations and their connectivity to convert raw materials to products. Superstructure optimisation represents a promising class of synthesis approaches, allowing for the systematic exploration of the flowsheet design space. Despite this, many superstructure formulations suffer from numerical instabilities, combinatorial explosion, and/or rely on restrictive assumptions on the types of flowsheet alternatives that can be considered. The modified state-operator network (MSON) formalism has recently been proposed to address some of these issues for isobaric flowsheets. The constant-pressure assumption restricts the applicability of the MSON to real process applications as pressure is a key process variable in many unit operations, such as distillation, reaction, and extrusion, and is necessary to elicit flow. In this work, we present the extended MSON (E-MSON) which inherits the numerical s... [more]

A topology optimization approach is applied to the design of a compact fluid distribution device that achieves uniform flow distribution among parallel channels while minimizing the mean residence time under a pressure loss constraint. Such devices are widely used in applications including parallel microreactors, thermal management systems, and energy devices, where both compactness and precise flow control are required. The design problem is formulated using a density-based method in which the design variable represents the local void fraction of a fictitious porous medium. The governing equations are based on the incompressible Navier-Stokes equations with an additional Darcy-type resistance term. The objective function is defined as the spatially averaged design variable, corresponding to the minimization of the effective flow passage volume and thus the mean residence time. Constraints are imposed on both flow rate uniformity among parallel channels and the total pressure loss. A t... [more]

In this study, we propose a method to uniquely determine robust operating conditions for simulated moving bed (SMB) chromatography, an essential continuous liquid-phase separation technique in the pharmaceutical industry, in the form of explicit algebraic equations. The proposed method incorporates process robustness-defined as the probability of meeting the target purities under flow-rate uncertainty due to pump errors-without requiring computationally expensive dynamic simulations. In a computational demonstration, the method achieved a joint probability of 0.960 for simultaneously attaining 99.9% purity in both extract and raffinate products.

Anaerobic digestion (AD) across Europe and the UK faces increasing economic and operational pressure from volatile feedstock supply under climate extremes. Existing stochastic programming (SP) approaches for feedstock planning often rely on limited historical observations and/or simplify yield uncertainty in ways that miss the joint, non-linear response of crops to weather variability, thereby understating downside supply risk. We develop an integrated decision-support framework that links climate uncertainty to AD procurement planning by coupling mechanistic crop simulation, generative surrogate modelling, and stochastic optimization. First, APSIM is used offline to generate a mechanistic yield knowledge base across weather trajectories and discrete planting-density choices. Then, a conditional GAN (CGAN) is trained to produce non-parametric joint yield samples for multi crops conditioned on scenario features and management, enabling fast Monte Carlo evaluation. At last, these samples... [more]

Aluminium cold rolling is an energy-intensive process that has a substantial impact on CO2 emissions and production cost, yet plant-level optimization remains challenging due to strong process nonlinearities and various operational constraints. This study develops a physics-consistent hybrid model that combines a Stone-Hitchcock-Ludwik analytical rolling-energy formulation with a residual deep neural network to predict the daily electricity consumption of three single-stand cold rolling mills. Using plant raw data, the hybrid model achieves lower prediction errors than conventional data driven model and yields line-specific physical parameters that agree well with the observed behaviour of each mill. On this basis, an NSGA-II-based tri-objective optimization is carried out to minimise daily energy use, CO2 emissions, and specific production cost (SPC) by adjusting pass-wise reduction and tension schedules and line-wise production allocation. Case studies on a representative operating d... [more]

To improve the energy efficiency of distillation processes, various process intensification concepts have been proposed, including direct heat integration and thermal coupling. Identifying the most suitable alternative for a given separation task requires a rigorous and consistent techno-economic optimization. Superstructure models typically rely on isobaric operation and fixed HETP values, in order to avoid treating column hydraulics when solving the already challenging mixed-integer nonlinear optimization problems. In order to overcome this limitation and evaluate the effect of the simplification, the current work extends a rigorous equilibrium-stage superstructure model to account for tray-specific pressure drop and HETP values. A polylithic solution approach is implemented to improve the convergence for the resulting optimization problems. The proposed approach is demonstrated for the optimization of heat-integrated distillation sequences operated at close to atmospheric and vacuum... [more]

The pipeline transportation and distribution process of natural gas, from production sources to end consumers, can be divided into three stages: upstream production and supply, midstream storage and pipeline transportation, and downstream distribution and end-use. In the optimization of natural gas pipeline networks, considering the full life cycle, multiple uncertainties in planning, design, operation, and maintenance often affect the efficiency and quality of model optimization. This study addresses the uncertainty in end-user demand during the operation of natural gas pipeline net-works and investigates a scheduling method that simultaneously meets user demand while achieving coordinated optimization of operational costs and carbon emissions. Based on one year of historical demand data, a normal distribution is fitted to characterize the demand, and several representative scenarios with corresponding probabilities are extracted through clustering to capture demand uncertainty. A two... [more]

Batch production scheduling of resources to meet fluctuating product demand is a critical topic in the process industry. Existing optimisation approaches, based on heuristic and exact methods, trade off solution optimality and scalability to large problems. In this work, we investigate deep reinforcement learning as a powerful alternative in order to learn heuristics for batch scheduling. We formulate the batch scheduling problem as a Markov decision process operating on a state-task network representation encoded using graph neural networks, capturing relevant structural inductive biases. We propose a centralised training with decentralised execution architecture, in which agents placed on machines individually choose which tasks to complete using a global view of the network, cooperating towards task schedules that optimise the final production quantity. Preliminary results demonstrate that the proposed end-to-end framework learns to construct task schedules comparable to the optimal... [more]

This work aims to systematically generate intensified process designs by integrating reinforcement learning (RL)-driven process synthesis and phenomena-based modeling via Generalized Modular Framework (GMF). Rather than considering flowsheet synthesis with conventional unit-operations, GMF utilizes fundamental building blocks, also known as mass and heat exchange modules, to describe the physiochemical phenomena and to enhance novel process discovery. At its core are driving forces which characterize the mass transfer feasibility based on the total change in Gibbs free energy of the system. RL is integrated with this phenomena-based modeling strategy to drive flowsheet generation by exploring much of the total action space and minimizing pre-postulation of stream connections. All possible inlets, outlets, and interconnections between modules are contained in a stream matrix. Deep Q-Network is used as the RL agent, which contains a multi-layer convolution neural network followed by a mu... [more]

Although solvent-based crystallization is widely adopted for separation and purification of crystalline pharmaceutical products, solvent choice and utilisation critically influence product quality, manufacturing cost, and the environmental performance of the pharmaceutical process. Escalating demands to reduce process mass intensity (PMI), together with increasing vulnerabilities in the supply chains, necessitate the development of more efficient and resilient process designs, incorporating solvent and active pharmaceutical ingredient (API) recycling. The conceptual design of crystallization processes offers a viable route to identify flowsheets with substantially reduced solvent consumption. In this paper we present a computer-aided molecular and process design (CAMPD) formulation to explore the benefits of solvent/API recycle for two processes/APIs: (i) a continuous cooling crystallization process for mefenamic acid (MA) employing a binary solvent mixture and (ii) a batch cooling cry... [more]

In recent years, research for systematic process design approaches has gained traction, especially with the rise in popularity of generative machine learning models and reinforcement learning. However, works from the literature will often focus on proof-of-concept studies, limited to a specific process synthesis problem. Despite showing promising results, it is not clear how easily these methodologies could be transposed to new applications, and whether they would be successful. In this context, this work evaluates the possibility of using a Natural Language Processing model, which has already proven itself for thermodynamic cycle generation, for another different case: seawater desalination. The processes generated by this model will initially be those using reverse osmosis processes aimed at desalinating a seawater solution containing 25000 ppm of NaCl. Results show that the model has been successful in designing structural reverse osmosis desalination processes without defining asse... [more]

Heat Pumps (HPs) can play a vital role in the decarbonization of heating in industry. The performance of a HP strongly depends on the refrigerant, the working fluid within the HP. In order to maximize HP performance, systematic selection of the refrigerant is key. Refrigerant choice affects the very feasibility of employing a HP to deliver heating to a process. A flexible and robust method is required to select refrigerants that are the best fit for a given heating application. A computer-aided molecular & process design (CAMPD) method is developed to design the optimal refrigerant that is tailored to process needs. The method is applied to three case studies across which the HP performance objectives and constraints, and heat source and heat sink temperatures are varied. In addition, the design of refrigerants with low (<150) global warming potentials and zero ozone depletion potentials is investigated. For all applications across all case studies, the CAMPD approach successfully iden... [more]

An optimized flowsheet can be generated by numerous approaches. Process optimization via superstructure is one of the methods used to provide solutions that consider the interactions between different decision layers. A process simulator-based optimization is considered in this work, as it offers a reliable and rigorous modeling environment. It is then coupled with a P-graph-based framework to reduce the tedious mathematical writing of the logical constraints to guarantee the structural coherence of a sequence of unit operations.The developed framework consists of three algorithms. The first algorithm transforms the superstructure flowsheet into a P-graph. The second algorithm gets process sub-structures from the superstructure by searching for active units corresponding to a set of decisions made, for example, by an optimizer. The third one checks structural feasibility by verifying that the resulting structure satisfies the five axioms of the original P-graph framework and two additi... [more]

Industrial decarbonization is crucial to reducing global emissions. Efficient processes lower energy use and reduce the environmental impacts, such as material use and waste, decreasing the overall industrial footprint. In this context, the present study explores the impact of reducing steam consumption (thermal energy) during the ammonia regeneration process in the production of sodium carbonate. A key feature of the Solvay process is ammonia recycling, which significantly reduces raw material consumption and ensures both economic and environmental sustainability. However, this stage is highly energy-intensive. To enhance energy efficiency in soda ash production, a study was conducted to analyze variation in temperature, pressure, and steam flow introduced into the ammonia regeneration system. The objective is to understand its impact on both ammonia recovery and the process's energy consumption. Variations in steam pressure do not impact on energy consumption of the process. By reduc... [more]

Industrial ammonia synthesis remains a high-impact decarbonization target due to the combined effect of large production volumes and reliance on CO2-intensive fossil-based hydrogen. Replacing it partially with renewable-based (green) electrolytic hydrogen can minimize direct emissions; however, the intermittency of solar and wind complicates retrofit strategies for existing continuous plants. This paper addresses a techno-economic study centered on a retrofit-oriented hybrid concept: a conventional natural-gas ammonia plant is operated within a pre-defined "hybridization envelope", where part of the process hydrogen is supplied by a renewable Power-to-Hydrogen subsystem. A steady-state process model (gray/blue baselines) is coupled with an hourly energy dispatch and a sizing optimization of solar/wind park, electrolyzer, battery, and hydrogen storage. A case study based on 2024 California data for renewables and carbon pricing illustrates how hybridization can reduce carbon intensity w... [more]

Grid-connected electrolyzers with intermediate hydrogen storage offer significant potential for reducing electricity costs through flexible operation under dynamic pricing. A threshold-based scheduling optimization approach is developed that derives interpretable on/off production rules from electricity price signals. The method identifies local price thresholds separating high-price from low-price periods, yielding binary production schedules. Adaptive horizon partitioning-subdividing the scheduling horizon when constant thresholds become infeasible-is combined with a receding horizon strategy that implements only a portion of each optimized schedule before re-optimization. This procedure enables systematic investigation of how characteristics of Integrated Electrolyzer-Storage Systems (IESS) influence cost reduction potential while maintaining computational tractability for both offline analysis and online implementation. A case study applying the approach to historical German electr... [more]

To effectively distribute power to a system of multiple electrolyzer stack units, control strategies have been developed that now need to be applied to heterogeneous electrolysis systems. These are the 'segment principle', the 'slow start principle' and the 'start-stop principle'. As there are many possible combinations to the system composition of a modular heterogeneous electrolysis system together with the most suitable control strategy, a discrete multi-criteria optimisation problem can be formulated. To solve this discrete multi-criteria optimisation problem, two discrete decision variables are introduced. One is the electrolysis system composition, represented by the power ratio/configuration (C). A total of 17 different configurations were used for this, consisting of different proportions of alkaline electrolysis (AEL) and proton exchange membrane electrolysis (PEMEL). The other one are the control strategies (R). For the control strategies, the conventional strategies, mention... [more]

Today, beverage production plants are planned and designed from the material-handling context as a packaged-goods production facility, not as a process plant. Therefore, a lot of potential for optimization exists. This paper presents a new approach to the design of beverage production plants according to the design of process plants. A component library for the simple creation of beverage production plant process models is developed. All steps in the plant design process can be accelerated and automated to be used for the high number of existing and new installations around the world. As first use case an energy optimization upgrade for existing Carbonated-Soft-Drink production lines is described to save cooling and heating energy in warm climates.

In the pursuit of decarbonisation, process industries are turning to electrification as a solution to avoid fossil fuels for heating and processing raw material. Transitioning to renewable electricity couples the processes to varying electricity availability and requires more consideration for production timing and scheduling to support grid stability and avoid high electricity prices. However, practical challenges limit the capability for unforeseen rescheduling for large processes. This paper explores the idea of auxiliary flexibility in an electrified steel production process, where only the auxiliary systems can react to changing conditions. We model an H2-DRI-EAF inspired process with controllable Air-Separation unit, water electrolysis, pressurized hydrogen storage, gas liquefaction units, and a battery energy storage system to react to a production related demand delay. First, we compare hourly and 15-minute DA pricing and observe that without fast flexibility the cost differenc... [more]

For process simulation, Python and Aspen Plus can be combined to leverage the advantages of both. This work utilizes the integration of Python and Aspen Plus for the design and optimization of a lycopene production process from tomato waste. Three production pathways are studied: acetone and hexane as solvents, enzymes with ethyl acetate, and supercritical CO2 with ethanol. This allows for the scaling of laboratory-scale studies into industrial-scale analyses. Genetic algorithms are used for optimization, enabling the determination of the optimal process design, costs, and operating conditions, while minimizing the total annual cost. The process with acetone and hexane yields a final production of 0.21 kg/h, the process with enzymes and ethyl acetate, 5.13 kg/h, and the process with supercritical CO2 and ethanol, 0.13 kg/h. It is shown that the process with ethyl acetate has a higher production and the process with supercritical CO2 and ethanol results in lower production and higher co... [more]

Manufacturing disruptions can be a major driving factor in the wastage of resources and delays which result in spiralling costs and cancelled orders. Operational decision making should therefore consider the potential for disruptions from as many sources as possible, encouraging improvements to operational resilience and agility. Our work presents a scheduling and rescheduling framework formulated as a rolling horizon problem for the emulation of real time decision making within a dynamically changing scenario. The framework is applied to a complex multistage problem with parallel lines susceptible to disruptions as a result of process or equipment failures, or ineffective inventory management that results in material shortages. The framework is demonstrated for a simple example case which highlights the impact of disruptions on the time taken to complete orders and the associated costs. It is observed that the inclusion of disruptions can alter equipment congestion, shifting focus for... [more]

Decarbonizing ammonia by replacing grey with green hydrogen directly affects the operation of the Haber-Bosch (HB) process. When directly coupled to green hydrogen production from renewable energy, the HB process should be able operate flexibly to match variable hydrogen supply. This study presents a structured approach for designing a load-flexible HB plant, supported by a rigorous process model. First, we screen 2, 000 designs at high (100%) and low (10%) hydrogen loads to assess operability. Only 1, 100 designs are feasible for both loads, underscoring the need to account for multivariable interactions during design. Next, we assess the economic feasibility of a base design, comparing HB operation under constant and flexible loads. Flexible operation reduces the levelized cost of ammonia (LCOA) by about 5.8%, primarily by lowering green hydrogen production costs. This cost reduction results from downregulating hydrogen production during periods of high electricity prices. By contras... [more]

The integrated recovery of heat and mechanical work has gained increasing importance in process integration due to the strong thermodynamic coupling between temperature and pressure changes in many industrial systems. This work presents a rigorous framework for the simultaneous synthesis of Work and Heat Exchanger Networks (WHEN), in which heating, cooling, compression, expansion, throttling, and pumping are optimized in a coordinated manner. The problem is formulated using Generalized Disjunctive Programming (GDP), allowing the explicit representation of alternative thermodynamic paths, phase-dependent behavior, and logical equipment choices. Process streams are defined by supply and target states, while only bounds are imposed on intermediate pressures, temperatures, and flow rates. Streams may change classification between hot and cold multiple times and may undergo several phase transitions.Rigorous thermodynamic correlations obtained from Aspen HYSYS are embedded in the optimizati... [more]