The Imperial College Integrated Design Project reframes the chemical engineering capstone as a structured educational journey that develops professional competence rather than simply delivering a final technical report. The programme is grounded in four pedagogical pillars-authenticity, integration, impact, and reflection-which align with the graduate attributes required by the Institution of Chemical Engineers. Authenticity is achieved through open-ended problems drawn from industrial partners and emerging research needs; integration connects knowledge from across the curriculum into a coherent systems perspective; impact emphasises user-centred, sustainable solutions; and reflection cultivates metacognitive awareness of decision making and learning from failure. A mentored-autonomy model supports student teams through weekly checkpoints, skills workshops, and access to disciplinary experts. Assessment deliberately balances artefact quality with evidence of process, rewarding reasonin... [more]

Modern semiconductor manufacturing requires extreme precision as yield margins narrow in the "More-than-Moore" era. While physics-based models (PBMs) provide high-fidelity insights into plasma etching, their computational intensity-often requiring hours per simulation-renders them impractical for direct iterative optimization. This work demonstrates a hybrid framework that utilizes data-driven surrogate models to enable rapid, cost-effective process optimization. A 2D axisymmetric fluid model of an inductively coupled O2 plasma (ICP) reactor was developed to generate a training dataset for two neural architectures: a Multi-Layer Perceptron (MLP) and a Kolmogorov-Arnold Network (KAN). These surrogates predict radial etching rates across a wide operating window of power, pressure, gas flow, and bias voltage. By replacing the expensive PBM with these high-speed surrogates, derivative-free optimization algorithms (Nelder-Mead and Powell) successfully identified a profit-maximizing operatin... [more]

Carbon dioxide transport is a critical component of the carbon capture and sequestration (CCS) supply chain. Given the substantial energy requirements and dispersed locations of CCS facilities, optimizing pipeline operations is critical to minimize costs. Although CO2 in dense phase is typically favored for long-distance transport, gaseous phase transport is also a possibility for shorter distances and volumes. This study models a gaseous CO2 pipeline system. Since CO2 gas pipelines provide the benefit of line packing, owing to gas compressibility, this work leverages it to maximize throughput in the presence of disturbances. Pipeline pressures within each segment are perceived as an inventory (i.e. form of storage) and a model predictive control (MPC) formulation for optimal inventory management is implemented to maximize throughput. This study applies the formulation to pipelines arranged in series and parallel. It effectively maximizes throughput and optimally drains pipeline pressu... [more]

This research presents a novel framework for the surrogate-based dynamic optimization of control schemes within chemical separation and purification processes such as the biorefinery downstream processing. The current study investigated the downstream of an enzymatic bioreactor responsible for the synthesis of 5-hydroxymethylfurfural value-added derivatives, focusing on the critical balance between operational costs and productivity. Two high-fidelity long short-term memory neural network-based surrogate models were developed to predict energy consumption and economic gain, both achieving a coefficient of determination (R2) exceeding 0.97. These models were subsequently integrated into a multi-objective optimization architecture to address an operating efficiency testing scenario characterized by stepwise inflow parameter changes. By exploring the resulting Pareto front, an optimal set of operational (control) settings was identified and validated. The results demonstrate that while en... [more]

We study a data reconciliation problem in a petrol refinery. The problem is to reconcile inventory and flow measurements to estimate true values of measured and unmeasured flows respecting the mass conservation. The problem is formulated as a mixed-integer quadratic program (MIQP). Upon successful problem resolution, a neural network (NN) is trained to mimic the MIQP solver to study potential improvements in CPU time without compromising the solution quality. The results show a significant improvement in refinery monitoring and feasibility of NN-based reconciliation.

In modern industrial systems, increasing performance requirements and sustainability constraints have intensified the need for advanced optimization methodologies capable of efficiently handling complex process models. The Simulated Moving Bed (SMB) process is a well-established technology for continuous chromatographic separations, offering high productivity and reduced solvent consumption compared to batch operations. However, its optimization is challenging due to the underlying distributed-parameter nature of the process.This work presents the development of a dynamic simulation and parameter optimization framework for the SMB process, implemented in Python using the open-source CasADi framework. The SMB model accounts for axial dispersion and mass transfer using a linear driving force formulation and is discretized in space using the method of lines, resulting in a state-space representation compatible with CasADi's numerical tools. Model accuracy was validated by reproducing a be... [more]

The Dividing Wall Column (DWC) offers significant potential in saving both energy- and capital cost compared to conventional distillation sequences. However, there are some issues regarding flexibility and control that require attention in reducing the risks or uncertainties in achieving the potential benefits in practical operation. This calls for control and optimization methods that rely on the available measurement data and less on simulation models. The "Perturb and Observe" method is a simple algorithm that seems suitable for this on-line optimisation task. A series of experiments have been carried out at the Kaibel-column pilot at NTNU and some key results are presented. The method is combined with a conventional control structure at the regulatory layer.

The energy recovery of agro-industrial residual biomass offers a pathway to reduce fossil fuel emissions in thermal processes while valorizing waste. In practice, however, the primary bottleneck is logistical: feedstocks are geographically dispersed, with low bulk density and high moisture content, driving up collection, pretreatment, and transport costs. This work combines geospatial processing with mathematical optimization to design a multi-stage logistics network. The model incorporates intermediate densification options and technology selection (chipping, pelletizing, or briquetting) to supply one or more final waste-to-energy plants. The case study focuses on Northeastern Uruguay, considering forestry residues, meat-processing waste, and rice husks. We formulate a multi-period Mixed-Integer Linear Programming (MILP) model aimed at minimizing the total annualized cost, encompassing transportation, logistical operations, capital investment, and plant O&M, subject to supply constrai... [more]

The Heterogeneous Capacitated Vehicle Routing Problem (HCVRP) is a fundamental extension of the classical Vehicle Routing Problem in which customer demands must be satisfied using a fleet of vehicles with varying capacities and costs. In this paper, a novel and intuitive decomposition-based formulation for HCVRP is presented that decomposes the problem into two tractable subproblems: (i) a route generation and an optimal customer sequencing problem and (ii) a vehicle route assignment problem. In the first stage, all feasible customer combinations are constructed as routes, and for each route an optimization problem is solved to identify the optimal customer sequence that results in the minimum distance travelled. In the second stage, the optimal routes are selected, and vehicles are assigned using a mixed integer linear programming (MILP) formulation that minimizes the fixed cost of vehicle utilisation and total transportation costs, ensuring demand satisfaction for all customers while... [more]

Water supply in the countryside of São Paulo state, Brazil, is based on groundwater resources that can be contaminated with substances such as heavy metals or fluoride, requiring the usage of water treatment technologies such as Reverse Osmosis (RO); however, RO systems create a stream of high-salinity brine, with negative environmental consequences. Besides, regulatory constraints demand that well operations must be interrupted for a daily contiguous period. In this work, a Mixed-Integer Linear program (MILP) was implemented to define water network topologies and well exploitation schedules, under these downtime constraints, aiming the minimization of RO plant capacity (and, therefore, of brine discharges). This model was then applied to the water supply of a small city in the São Paulo countryside, with around 8000 inhabitants, where high fluoride concentrations warranted the implementation of an RO system. Demand variations between weekdays and weekends (with demands 52.7% higher) w... [more]

Membranes provide an efficient method for treating flue gases to capture CO2 from various point sources, achieving high recovery and purity rates. However, the lack of systematic process-level design tools has limited the translation of advanced membrane materials into large-scale technical and economic metrics. Thus, in this study, we present a superstructure model for the design of membrane-based carbon capture, both from highly energy-intensive industries and from power plants. The superstructure model enables the flexible design and global optimization of multi-stage membrane systems. Multiple membranes are compared under technical performance indicators (specific energy and specific area), while the already commercialized polymeric membranes Polaris and PolyActive are taken into consideration for estimating their economic performance. The presented framework establishes a robust link between material innovation and optimal process design, providing a key tool for the large-scale d... [more]

This study presents different approaches for introducing mixed integer problems into a meta-heuristic algorithm. The algorithms are developed to address the simultaneous design and optimization of a heat pump unit. A distinction is made between integer variables such as nominal tube diameters and the adsorbent employed in the process. The choice of adsorbent is named as a "key variable" due to its high impact on the process. To optimize the selection of these "key variables", a branched version of Particle Swarm Optimization (PSO) is presented and compared with the non-Branched version and a deterministic solver (IPOPT). Advanced Convergence Criterion is also implemented to mitigate the computational effort of these approaches. In the studied cases, Branch_PSO presents a higher degree of consistency and can even outperform the traditional PSO in simultaneous process optimization and material screening. However, its computational effort in cases with a large number of branches might be... [more]

The electrification of regional aviation requires coordinated planning of airport energy systems that integrate renewable generation, energy storage, and hydrogen technologies in a cost-efficient and resilient manner. This paper presents a scalable techno-economic optimization framework that models multiple airports as collaborative energy hubs. An object-oriented mixed-integer linear programming (MILP) formulation is combined with a genetic algorithm (GA) to optimize infrastructure sizing and energy dispatch. The framework is applied to three Swedish regional airports-Västerås, Jönköping, and Visby. A set of scenarios, including parties operating under shared wind-energy contracts using power purchase agreements (PPAs) and dynamic pricing (DP), was studied. Detailed representations of battery energy storage, hydrogen production and storage, and market interactions are included. Results show that coordinated operation and airport collaboration under a smart energy management system can... [more]

The scheduling of multi-line polyolefin production is a complex decision-making process characterized by sequence-dependent changeovers, strict physicochemical constraints, and dynamic market environments. Traditional optimization methods often suffer from high computational costs and a lack of flexibility in online adjustments. To address these challenges, this paper proposes a Deep Reinforcement Learning (DRL) framework for dynamic scheduling tasks. We first construct a high-fidelity simulation environment that meticulously models realistic industrial constraints, including transition materials, shutdowns, and inventory limits. A Soft Actor-Critic (SAC) agent with a tuple-based action space is employed to mitigate the combinatorial explosion associated with multi-line decisions. Furthermore, a dynamic action masking mechanism embedded with domain knowledge is introduced to strictly enforce hard constraints and significantly improve sample efficiency. Case studies based on real-world... [more]

The state task network (STN) representation is a widely used modeling approach for optimal multipurpose batch production scheduling. In practice, STNs have been traditionally formulated as mixed-integer programming (MIP) problems and solved using general-purpose MIP solvers relying on branch-and-bound and branch-and-cut. In the meantime, alternative modeling and solution paradigms for optimization have been developed, enabling the incorporation of alternative variable types and optimization algorithms. Specifically, this work relies on the Hexaly software, which introduced set-based models and their solution through general-purpose hybrid algorithms, i.e., methods that combine traditional MIP with constraint programming, local search, large neighborhood search, among other tools. So far, Hexaly has shown promising results when tackling optimal scheduling problems, however, set-based models and solution approaches for STN optimization have not been studied in the literature. Aiming to f... [more]

This paper presents a Europe-wide optimisation framework for designing and operating a multi-period Carbon Capture and Storage (CCS) supply chain across Europe. A MATLAB preprocessing pipeline constructs an auditable techno-economic dataset (emission nodes, ports, aquifers, candidate pipeline/shipping arcs and costs) and exports it to a GAMS optimisation model. The planning problem is formulated as a two-stage stochastic MILP, where scenario-independent first-stage decisions select discrete pipeline and shipping capacity bands and port operating modes, while scenario-dependent second-stage decisions allocate capture, transport and sequestration flows. Uncertainty is represented through correlated scenarios of capture unit costs for four capture technologies (CV=0.35, rho=0.8, Ns=20). To address the computational burden induced by inter-temporal binary investments and scenario replication, we apply a two-phase arc-screening heuristic: an LP relaxation on the full network identifies prom... [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]

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.

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]

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]

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]

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]

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]

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]