Prospective life-cycle design of emerging technologies is important in discussions of decarbonization and resource circulation strategies. This study demonstrates the role of computer-aided process engineering in reflecting technology information with appropriate granularity and accuracy using air conditioning as a case study. Process simulations involving heat exchangers (indoor/outdoor units), compressors, and expansion valves were developed to model air conditioners to quantify changes in performance and heat exchanger size as existing and alternative refrigerants are introduced. The process simulation results were incorporated into a material flow analysis and life cycle assessment to quantify the change in life cycle greenhouse gas (GHG) emissions through 2050 for each refrigerant installed. The results show that operational emissions dominate the life cycle GHG emissions of air conditioners, that decarbonization of electricity can significantly reduce life cycle GHG emissions, wi... [more]

The growing demand for sustainable alternatives to petroleum-based products drives the development of biomanufacturing using agriculture-based sugars. However, agricultural sugar production faces significant challenges due to limited production capacity and potential negative environmental impacts. This research examines chemical sugar synthesis as an alternative, assessing its environmental impact with conventional agricultural production methods through life cycle assessment. As formaldehyde serves as a primary substrate in chemical synthesis, four production cases were evaluated—comprising two pathways (conventional methods and CO2 capture and utilization (CCU) technologies), each implemented with either fossil fuels or renewable energy sources. The analysis revealed that semi-batch reactors in chemical synthesis substantially reduce environmental impacts compared to batch reactors. Chemical sugar synthesis demonstrated marked advantages in reducing eutrophication, land use change,... [more]

One of the most successful continuous high-performance liquid chromatography (HPLC) processes for drug manufacturing is the Simulated Moving Bed (SMB). SMB is a multi-column, continuous, chromatographic process that can handle much higher throughputs than regular batch chromatographic processes. The process is initially transient, but eventually arrives at a cyclic steady state, which makes optimization very challenging, even more so when superstructure optimization is involved. To simplify the optimization problem, many researchers fixed the SMB structure, optimizing only the continuous variables, so they cannot be considered superstructure optimization. In this work, an SMB superstructure that can simultaneously optimize column structure and operation is proposed. The results showed that the superstructure proposed is reliable, and it is more efficient compared to current optimization approaches if the optimal column structure has to be identified.

Process design deals with the problem of finding the best process set-up, subject to a set of constraints defining the design space (DSp). This selection is guided primarily by economic considerations. Flexibility may also play an important factor in process design, since it embodies “how far” from the design space’s bounds are the candidate optimal designs, which in some cases may lead to off-spec products. This work proposes a novel approach for flexibility assessment. In design problems where the design space is constrained by a set of affine bounds, flexibility may be expressed either as the minimum or the maximum distance with respect to the feasible (design) space bounds. For any point in the DSp, the minimum distance provides a good indicator on the minimum flexibility, as the direction that represents the highest risk of violating the constraints. An analogous conclusion can be drawn between the maximum distance and maximum flexibility. These distances can be computed exactly v... [more]

In this work, we introduce a quantum-enhanced reinforcement learning (RL) framework for process design synthesis. RL-driven methods for generating process designs have gained momentum due to their ability to intelligently identify optimal configurations without requiring pre-defined superstructures or flowsheet configurations. This eliminates reliance on prior expert knowledge, offering a comprehensive and robust design strategy. However, navigating the vast combinatorial design space poses computational challenges. To address this, a novel approach integrating RL with quantum machine learning (QML) is proposed. QML leverages theoretical advantages over classical methods to accelerate searches in large spaces. Built upon our prior work, the approach begins with a maximum set of available unit operations, represented in a flowsheet structure using an input-output stream matrix as RL observations. A Deep Q-Network (DQN) algorithm trains a parameterized quantum circuit (PQC) in place of a... [more]

Artificial Photosynthetic Bio-Electrochemical Systems (AP-BES) offer a promising approach for converting CO2 to valuable bioproducts, addressing carbon mitigation and sustainable production. This study employs a stochastic techno-economic assessment (TEA) to estimate the viability of rhodopsin driven AP-BES, from carbon capture to product purification. Unlike traditional deterministic TEAs, this approach uses Monte Carlo simulations to model uncertainties in key technoeconomic parameters, including energy consumption, CO2 conversion efficiency, and bioproduct market prices. The analysis generates probability distributions for economic metrics such as Operational Expenditure (OPEX), Capital Expenditure (CAPEX), and profit. Enhancements in light-harvesting efficiency and advancements in reactor materials were predicted to reduce the payback period to just one year, thereby making large-scale deployment a feasible option.

The green transition accelerates innovations and developments targeting the integration of green hydrogen in the chemical industry. However, all new hydrogen pathways and process designs must be tested on operability and safety. A big challenge is the typical fluctuating characteristic of green hydrogen supply that contrasts the steady-state operation of most conventional chemical processes. Therefore, to adequately assess control and monitoring techniques, a benchmark model tailored to the relevant aspects of the hydrogen economy is required. We introduce a benchmark model based on the production of green ammonia using the Haber-Bosch process that remains operable when coupled to a fluctuating hydrogen supply from water electrolysis. The main section of the process model is an adiabatic indirect cooled reactor system that provides realistic modeling of industrial applications. Like the ammonia reactor, all process units and the underlying control structure are precisely dimensioned to... [more]

Mechanical compression heat pumps are indispensable to facilitate the transition from thermally driven processes to renewable energy by electrification, upgrading low-temperature waste heat to recycle it at a higher temperature level. However, the implementation of such heat pumps up to date encounters limitations, due to equipment limitations and a lack of tools for the design of process concepts for the application of high-temperature heat pumps. The optimal design of heat pumps relies heavily on the selection of an appropriate refrigerant, as the thermodynamic properties significantly affect the heat pump cycle design and performance. While existing methods are capable of identifying thermodynamically beneficial refrigerants, they do not directly account for practical constraints such as limitations on the compressor discharge temperature, compression ratio, and vacuum operation. The current study proposes a fast-screening approach for arbitrary heat pump applications, considering a... [more]

In process systems engineering (PSE), obtaining accurate process models for optimization can be expensive and time-consuming. Black-box Bayesian Optimization (BO) with Gaussian process (GP) surrogates offers a promising approach. However, full black-box optimization neglects valuable prior knowledge, which could otherwise improve the optimization process. This work explores methods of integrating prior knowledge in the form of low-fidelity data into BO by evaluating these methods on synthetic multi-fidelity test functions. Our results highlight possibilities for improved convergence of the BO optimization. However, our work further highlights potential pitfalls of these multi-fidelity models, such as bias, convergence to local optima, and overfitting on low-fidelity data. Hence, leveraging low-fidelity data in multi-fidelity models can improve BO convergence, but there are instances where the algorithms are more susceptible to failure.

Hydrogen isotope separation is a critical component of the fusion fuel cycle, particularly for achieving the desired purity levels of deuterium and tritium while minimising tritium inventory. This study investigates the cryogenic distillation of hydrogen isotopes, with a focus on the effects of isotopic equilibrium reactions at reduced temperatures and different system configurations. A one-column architecture was analysed to evaluate the impact of feed and side stream equilibrator temperatures and flowrates on separation performance and tritium inventory. Additionally, a two-column architecture was studied, incorporating multiple isotopic equilibrators in interconnecting streams, to further reduce unwanted heteronuclear isotopologues and improve system efficiency. Comparative analysis of the proposed configurations highlights significant operational advantages of optimising equilibrator temperatures, including reduced tritium contamination and inventory. Results indicate that reducing... [more]

Industrial decarbonization is considered one of the key objectives in mitigating global climate change. To achieve a net-zero industry requires actively transitioning from fossil fuel-based energy sources to renewable alternatives. However, the intermittent nature of renewable energy sources poses challenges to a reliable and robust supply of energy for industrial sites. Therefore, the integration of renewable energy systems with existing industrial processes, subject to energy storage solutions and main grid interconnections, is essential to enhance operational reliability and overall energy resilience. This study proposes a novel framework for the design and optimization of industrial utility systems integrated with renewable energy sources. A monthly-based analysis is adopted to consider variable demand and non-constant availability in renewable energy supply. Moreover, carbon capture is considered in this work as a viable decarbonization measure, which can be strategically combined... [more]

Anthropogenic carbon-dioxide emissions, exceeding 51 billion tons annually, are a major driver of global climate impacts. Aqueous amine scrubbing offers an effective carbon-capture solution, but the energy-intensive thermal regeneration step of the process significantly increases costs, limiting large-scale adoption. To address these challenges, computational optimization of process and molecular design is promising but often too resource-intensive, emphasizing the need for efficient surrogate models. Specifically, we develop a surrogate model based on an artificial neural network (ANN) that is employed to replace rigorous phase-equilibrium computations performed with the SAFT-? Mie group contribution method within a steady-state aqueous amine carbon-capture process model. Our ANN is trained on 32,768 vapour–liquid equilibrium data points of a quaternary mixture of water, monoethanolamine, carbon dioxide, and nitrogen over industrially relevant temperature, pressure, and composition ra... [more]

Asserting the phase stability of multi-component mixtures is an important task in computer-aided mixture/blend design (CAMbD), but it is often hindered by the lack of reliable and tractable models. In this paper, we propose a group-contribution machine-learning (GC-ML) method to predict phase coexistence for a large set of ternary mixtures consisting of two solvents and one (fixed) solute. Each solvent is represented by a vector of functional group numbers, encoded by integer values. The solvent vectors are combined with mixture composition and temperature to form the input features to a GC-ML surrogate classifier, which distinguishes between four types of stable phase configurations as possible outputs: liquid (L), solid-liquid (SL), liquid-liquid (LL) or solid-liquid-liquid (SLL). To explore the performance of the trained GC-ML multi-classifier, it is embedded as a surrogate phase-stability constraint in the optimisation of an ibuprofen crystallisation process. A two-step solution s... [more]

Recently, the focus on increasing process efficiency to reduce energy consumption has driven the adoption of alternative systems, such as Petlyuk distillation columns. It has been proven that, when compared to conventional distillation columns, these systems offer significant energy and cost savings. From an economic standpoint, achieving high-purity products alone does not ensure the feasibility of a process. Instead, balancing the trade-off between product purity and cost necessitates multi-objective optimization. While conventional optimization methods are effective, novel strategies like Bayesian optimization offer distinct advantages for handling complex systems. Bayesian optimization requires no explicit mathematical model and can efficiently optimize even when starting from a single initial point. However, as a black-box method, it demands a detailed analysis of hyperparameters, such as the acquisition function and the number of initial points, to ensure optimal performance. Thi... [more]

Liquid-liquid extraction (LLX) is an essential technique for separating heat-sensitive, highly diluted, or azeotropic mixtures. However, the design and optimization of LLX processes can be challenging due to mass transfer limitations and complex fluid dynamics. While distillation can often be modeled using equilibrium-based (EQ-based) approaches with (constant) height equivalent to theoretical stage (HETS) values, these kinetic effects can limit the applicability of EQ-based LLX models for conceptual design. Non-equilibrium (NEQ) or rate-based modeling can account for detailed mass transfer and fluid dynamics but further increases the nonlinearity and complexity of the respective optimization problems, which should account for closed-loop solvent recovery. To successfully address these complexities, we propose an integrated methodology combining NEQ-based simulation with EQ-based superstructure optimization to design a hybrid extraction-distillation process. An NEQ model is first used... [more]

Fossil fuel reliance in the transportation sector remains a leading contributor to global greenhouse gas emissions, underscoring the urgent need for renewable alternatives like biodiesel. Derived from renewable feedstocks, biodiesel can reduce emissions, enhance energy independence, and support rural economies. However, its production faces challenges such as low energy efficiency, process optimization barriers, and the limited utilization of byproducts like glycerol, which elevate costs and hinder large-scale adoption. This study addresses these challenges by developing an integrated framework for biodiesel production and byproduct valorization, supporting the long-term decarbonization of biofuel production. Three key feedstocks—refined palm oil, rapeseed oil, and soybean oil—are evaluated for biodiesel yield. The single-step transesterification process is enhanced through a two-stage approach, optimizing fatty acid methyl ester conversion under varying methanol and NaOH catalyst spli... [more]

In response to global efforts to reduce carbon emissions, the oil refining sector, a major source of industrial emissions, has set ambitious decarbonization targets. This study analyzes trade-offs between minimizing CO2 emissions and costs through the use of Pareto optimal solutions. A superstructure optimization framework evaluates various technological pathways and timelines, employing a bi-criterion optimization approach using the ?-constraint method. Results show that cost-effective, higher-emission solutions often involve natural gas-based technologies with carbon capture, while expensive, low-emission solutions favor electric power-based technologies. The analysis incorporates detailed assumptions about grid carbon intensity of varying degrees and accounts for varying national policies. Comparative case studies across locations highlight how grid carbon profiles influence optimal strategies, providing insights to inform local policies and incentivize technologies.

Green hydrogen will play a key role in the decarbonization of the steel sector via the direct reduction path [1]. To meet the demand side, both a highly efficient numbering-up based scaling strategy for water electrolysis is needed as well as flexible operation strategies that follow the fluctuating electricity load. This paper presents a modularization approach for electrolysis systems that addresses both aspects by combining different electrolysis technologies into one heterogeneous electrolysis system. We present a modular design of such a heterogeneous electrolysis system that can be scaled for large-scale applications. The impact of different degrees of technological and production capacity-related heterogeneity is investigated using system co-simulation to find an optimal solution for the goal-conflict, that the direct reduction of iron for green steel production requires a constant stream of hydrogen while the renewable electricity profile is fluctuating. For this use-case the d... [more]

The development of Carbon Capture, Transport, and Storage (CCTS) and hydrogen pipeline networks is crucial for achieving deep decarbonisation in industrial sectors. However, existing network design models often assume perfect foresight, limiting their applicability to real-world infrastructure planning, which is inherently uncertain and iterative. This study introduces a novel rolling-horizon methodology for pipeline network expansion, leveraging a genetic algorithm-based approach that allows for adaptive routing and incremental infrastructure development. By comparing rolling-horizon designs to 2050-optimised networks in a case study of the Humber region in the UK, the analysis highlights the trade-offs between adaptability and cost efficiency. Results indicate that while rolling-horizon approaches better reflect real-world decision-making, they also introduce inefficiencies, increasing capital expenditures by approximately 8% for both hydrogen and CCTS infrastructure. Additionally, t... [more]

This work develops process options using a novel protonic membrane reformer (PMR) and liquefaction-based CO2 capture process for low-carbon hydrogen production from natural gas. Several hybrid concepts of the PMR and liquefaction process are suggested based on the strategies to handle the residual gas from the reformer. The process intensification and optimization results indicate that the hybrid system with a water-gas-shift reactor and off-gas recycling guarantees high H2 and CO2 recovery rates for the PMR operating at relatively low H2 recovery. The hybrid concept also has 74% energy conversion efficiency, which is higher than a conventional steam-methane reforming (SMR)-based H2 production with chemical absorption CO2 capture.

The rising levels of carbon dioxide (CO2) in the atmosphere significantly contribute to climate change, highlighting the need for effective CO2 mitigation strategies. While capturing and storing CO2 is important, converting it into useful products offers additional environmental and economic benefits. One promising method is the reverse water gas shift (RWGS) reaction, which transforms CO2 into carbon monoxide (CO). Membrane reactors (MR), which integrate selective membranes with equilibrium limited chemical reactions, have the potential to intensify processes based on the RWGS reaction. In such reactors, by-products like water are removed in-situ from the reaction zone, effectively shifting the reaction equilibrium to favor higher CO2 conversion. This study develops a comprehensive multi-scale mathematical model for RWGS membrane reactors. We integrate the microscale permeance model (for LTA-4A membrane) with the RWGS MR unit scale and the system’s scale models. The effectiveness of a... [more]

Direct Air Capture (DAC) technology has gained significant attention as a promising solution for mitigating CO2 emissions and meeting climate goals. However, the current challenges of high energy demand, capital costs, and scalability present critical challenges to the widespread deployment of DAC systems. Integrating DAC with Heating, Ventilation, and Air Conditioning (HVAC) systems in buildings offers a potential solution by enhancing indoor air quality while capturing CO2, thus lowering energy consumption and capital investment compared to standalone DAC systems. This study evaluates the techno-economic performance of an integrated DAC-HVAC system against a standalone DAC system. This analysis combines thermodynamic estimation of CO2 and H2O loadings and energy requirements with an economic evaluation of capital and operating costs to calculate the levelized cost of CO2 capture (LCOD) for both DAC-HVAC and DAC-standalone. A sensitivity analysis explores the effects of varying climat... [more]

This study investigates the development of a probabilistic design space (DS) for a tableting process, focusing on the uncertainty in critical model parameters. A an empirical model is used to assess the impact of critical process parameters (CPPs), including lubrication extent and porosity, on tablet tensile strength (CQA). By incorporating Monte Carlo and Bayesian techniques, the uncertainty of five model parameters is propagated, allowing the estimation of feasibility probabilities for achieving CQAs with a reliability greater than 0.95. The resulting probabilistic DS provides manufacturers with a tool to assess the likelihood of meeting CQAs under varying production conditions. The findings indicate that specific combinations of lubrication rate and porosity define a robust DS within the acceptable operating region, ensuring consistent tableting performance even in the presence of uncertainties. This approach emphasizes the importance of probabilistic DS in optimizing manufacturing... [more]

Hydrogen (H2), as the promising alternative to fossil fuel-based energy carriers, faces the critical challenge of diversifying its sources and lowering production costs. Biogas, produced from organic waste, offers a renewable and carbon-neutral option for H2 production, but its high CO2 content requires a pre-separation process of CO2 from CH4 or specialized catalysts for use in existing reforming processes. Chemical looping reforming (CLR), as an advanced H2 production process, uses an oxygen carrier (OC) as the oxidant, allowing raw biogas to be used directly in the reforming process. Recently, numerous studies on CLR design and analysis have demonstrated their growing economic feasibility. However, deploying the CLR process in the biogas treatment industry requires further research to analyze its technical, economic, and environmental performance under target capacities and H2 purity. This study proposes biogas-based CLR processes and analyzes the capability of the processes from te... [more]

This work presents a robust flowsheet design for the recovery and purification of waste solvent streams containing ethyl acetate (EtAc), methanol (MeOH), and water. Separation of this mixture is challenging due to the presence of two azeotropes: a homogeneous EtAc-MeOH azeotrope and a heterogeneous EtAc-water azeotrope. These azeotropes create a distillation boundary that divides the ternary composition space into two distinct regions, making separation via conventional distillation difficult. Additionally, the wide variability in waste solvent compositions requires a versatile design, as flowsheets optimized for dilute mixtures may not be feasible for concentrated ones. The key to this design is using a liquid-liquid extractor (LLX) with recycled water as the solvent, ensuring the mixture remains within the liquid-liquid equilibrium (LLE) split region, which facilitates spontaneous separation across the distillation boundary and promotes energy-efficient separation. The raffinate comp... [more]