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.

A safety-intelligent framework is presented for developing a multiple-input multiple-output (MIMO) risk-based explicit model predictive control (R-eMPC) for metal hydride storage systems (MHSS). These systems are susceptible to thermal runaway during the charging process as a result of the exothermic adsorption reaction within the metal alloy. To address this issue, deterministic and stochastic safety-intelligent control algorithms are designed and implemented by explicitly embedding a dynamic risk index (RI) or risk tolerance (() into the control law and decision-making. In closed-loop analysis, the deterministic R-eMPC regulates both core temperature and hydrogen storage capacity by forecasting fault occurrence, triggering alarms, and reducing the risk index by adjusting the optimal control actions, supply pressure and water flowrate. Meanwhile, the stochastic R-eMPC accounts for uncertainties in core temperature variation by incorporating risk tolerance through chance-constraints. W... [more]

This work demonstrates the application of a pulsed microwave system for single-step co-production of ethylene and ammonia from methane. To mitigate inherent production fluctuations from pulsed microwave reactors, a staggered manifold configuration was utilized to stabilize effluent flow for industrial-scale compatibility. Dynamic validation of the ammonia and ethylene purification columns confirmed that a rigorously tuned control strategy effectively rejects ±10% feed disturbances while maintaining process stability and product purity. Ultimately, this systematic approach establishes a robust foundation for the sustainable, electrified production of foundational chemicals by bridging the gap between laboratory-scale pulsing phenomena and industrial-scale operational reliability.

Centralized monitoring methods experience reduced fault detection sensitivity in large-scale industrial systems due to the masking effect arising from the aggregation of many interconnected variables. Decentralized monitoring, where variables are grouped into subsystems, has been shown to effectively address these limitations. However, the performance of this class of methods critically depends on how the network is partitioned, and the role of its structural factors on fault detection remains poorly understood. This work studies how network topology and causal structure affect decentralized monitoring in high-dimensional systems. Using SimCaNet, a DAG-based data simulator, where large-scale systems with 100-1000 variables were generated, we rigorously compared the performance of centralized and decentralized causal log-likelihood monitoring methods under process perturbations and sensor bias faults. Network partitioning is performed using the Leiden community detection algorithm and c... [more]

For the long-cycle process industry, operational cycles can be severely affected by equipment aging, catalyst deactivation, and safety limitations. As illustrated by the residue hydrotreating process, metal impurities gradually deposit on the catalyst during residue purification, leading to catalyst poisoning and eventual process shutdown. Such long-cycle processes require dynamic adjustments of operating conditions to balance immediate economics with long-term sustainability. While current practice relies on empirical tuning based on historical data, this work focuses on studying how to obtain an optimal operating trajectory to guide the monthly adjustments of operating variables. The long-cycle simulation of the residue hydrotreating process can be performed using the commercial software, PetroSIM. After adjusting the feed conditions, its embedded mechanistic model can calculate the deviation of average bed temperature from the set point and output the remaining operating time. Since... [more]

This study investigates control strategies for the flexible operation of heat pump-assisted distillation processes, focusing on the heat integrated distillation column configuration. The methanol/water separation system was selected as a case study and modelled to achieve 99.9 wt% AA-grade methanol purity. A limiting piece of equipment for flexible operation of heat pump assisted distillation is the compressor. To assess its impact on flexible operation, dynamic simulations in Aspen Dynamics were conducted for two heat integrated distillation column control strategies: one using fixed compressor duty and one using variable compressor duty. The control performance for a 20% throughput disturbance, as well as for a 50% turndown ratio scenario was investigated. Results show that fixed-duty operation maintains robust stability and rapid disturbance recovery even at 50% turndown, while variable-duty operation delivers higher efficiency for moderate load changes but cannot sustain low-load s... [more]

Brazil is the world's largest producer of orange juice, generating significant peel residues that are currently underutilized. This study proposes a mixed-integer linear programming (MILP) framework for optimal supply chain design, utilizing Special Ordered Sets of type 2 (SOS2) to accurately represent non-linear investment costs. The model maximizes Net Present Value (NPV) by integrating production costs with multi-echelon logistics, including inland transport, port handling, and international maritime freight. Applied to a case study in São Paulo, the framework evaluates pathways for the co-production of D-limonene, pectin, and bioenergy. Results indicate a positive NPV of BRL 1.27 billion, with pectin contributing over 65% of total revenue. The optimization favors centralized configurations in Araraquara or Matão to exploit economies of scale while minimizing the transport of high-volume, wet biomass. Notably, total transportation costs represent only 1.13% of expenditures, as the h... [more]

Production scheduling in the process industry is often treated as a static optimization problem, although real plants require frequent rescheduling due to disturbances such as rush orders, equipment breakdowns, and changes in processing times. This paper compares a simulation-optimization approach that couples a discrete-event simulator with an evolutionary algorithm (EA) with a sequence-based mixed-integer linear programming (MILP) formulation for real-time scheduling of multistage batch systems. Both methods are embedded in an event-driven rolling-horizon framework under strict computation time limits.In static experiments for a 3-stage, 2-machine flow-shop setting (10 products, 20 orders, random processing times), the EA achieved lower makespans across all tested time budgets, improving results by about 7-13% on average compared to the MILP approach. In real-time experiments (40 initial orders, maintenance, three rush orders, 10 s and 60 s periodic updates), the solution quality of... [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]

Critical metals are essential for clean energy technologies but, due to being mainly sourced through mining, the critical metal supply chain is susceptible to geopolitical risks. Electronic waste (e-waste), however, can serve as an alternative "urban mine", but the recovery at high purities requires complex and resource-intensive processing. This work explores the modeling and optimization-based design for the intensification of liquid-liquid extraction in small channels as a means to recover critical metals from e-waste. Small channels can achieve high mass transfer rates while mitigating the environmental impact. A superstructure-based approach is employed to represent the alternative system configurations, while a plant propagation algorithm is used to optimize the multi-objective problem to recover Neodymium (Nd) and Samarium (Sm). The multi-objective problem aimed to tackle product quality, process economics, and environmental impact. The results demonstrated that optimally design... [more]

Pyrolysis, a key waste-to-X technology, enables converting a wide portfolio of biomass waste into valuable chemicals and fuels. However, raw pyrolysis oils are chemically and physically unstable. A multi-step stabilisation is necessary to reduce acidity and the content of reactive components, mainly carbonyls and carboxylic acids. During stabilisation, which involves deoxygenation and hydrogenation as the main steps, an aqueous phase is generated as a by-product. This stream contains mainly water, but relevant amounts of methanol and ethanol (2-8 wt%) are also present, together with minor concentrations of higher alcohols, C1-C4 carboxylic acids, and light esters. The aim of this work is to design and optimise a process to isolate the methanol and ethanol embodied in the aqueous phase and exploit them as intermediates to generate biofuels, biochemicals, and pharmaceutical products. The process consists of a train of four distillation columns to maximise the recovery rate and purity of... [more]

This work proposes a two-stage optimization framework for the optimal utilization of biogas from pig manure, integrating process-level design with short-term operational planning under dynamic electricity tariff schemes in Mexico. In the first stage, a multi-objective optimization based on 3E (Exergy, Environment, and Energy) analysis was performed. The results demonstrate that increasing the biogas split fraction for upgrading significantly reduces the environmental and exergy indices, enhancing thermodynamic and environmental performance without compromising the energy index. High upgrading flows (split > 0.7) emerged as the most favorable compromise across the evaluated metrics. In the second stage, support vector regression (SVR) surrogate models were developed to approximate nonlinear relationships between the operational split and process outputs. These surrogates were embedded in a Mixed-Integer Linear Programming (MILP) formulation to optimize weekly scheduling under the Mexica... [more]

Self-driving laboratories (SDLs) increasingly use Bayesian optimisation (BO) to navigate expensive design spaces, yet high-fidelity simulations and experiments remain too costly to query at scale. Multi-fidelity Bayesian optimisation (MFBO) alleviates this by combining abundant low-fidelity evaluations with scarce high-fidelity observations. However, Gaussian process (GP) surrogates can become computational bottlenecks as data volume and dimensionality increase, motivating scalable alternatives. Here, we assess transfer learning based deep neural network (DNN) surrogates that pretrain on low-fidelity data and fine-tune on high-fidelity observations. We construct a chemical process benchmark for glacial acetic acid separation and purification with paired low- and high-fidelity flowsheets. The optimisation considers eight decision variables and minimises the minimum selling price (MSP), while enforcing a product purity threshold via a quadratic penalty. To reflect realistic resource cons... [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 work presents an integrated geospatial-technoeconomic optimization framework for siting modular blue and green hydrogen production units co-located with hydrogen refueling stations (HRS) along U.S highways, with a case study focused on New England. The workflow identifies geospatial highway networks and natural gas infrastructure intersections, estimates hydrogen demand based on heavy-duty truck flows from U.S. Freight Analysis Framework, and formulates a mixed-integer linear program (MILP) that selects technology candidates and their capacities to minimize total cost, subject to corridor coverage and supply-demand constraints. Two onsite hydrogen production scenarios are evaluated: a green hydrogen-only production case and a mixed configuration combining modular green and blue hydrogen. Results indicate that, under a 5% hydrogen adoption scenario in truck traffic, 29 HRS with onsite hydrogen production are needed in the New England region. These findings highlight the benefits of... [more]

This study presents a techno-economic modeling framework integrating a modular blue H2N2 production unit with a hydrogen refueling station (HRS) across capacities ranging from 0.1 to 4.0 tpd. A model-based approach is used to size key process and refueling components and to estimate the resulting hydrogen retail cost. The analysis indicates that hydrogen retail costs range from 4.6 to 10.8 USD kgH2-1 over the considered capacity range. Relative to alternative on-site hydrogen production pathways, the proposed system demonstrates better cost-effectiveness while meeting clean hydrogen production standards. The approach is particularly suitable for regions with established natural gas infrastructure, as it leverages existing supply chains. Overall, the results provide actionable insights for policymakers and industry stakeholders in planning future hydrogen refueling infrastructure.

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 optimization of chemical reactions requires navigating a high-dimensional design space composed of both discrete and continuous variables. Although one-hot encoding has been widely adopted, it lacks chemically meaningful information and suffers from sparsity and poor generalization. To address these limitations, we explored the use of pretrained molecular foundation models to generate latent representations as input variables for optimization. However, rigorously comparing different combinations of reaction representations and optimization algorithms remains a time- and resource-intensive challenge. In this work, we developed an end-to-end benchmarking platform that systematically evaluates diverse encoding schemes and optimization strategies under identical conditions. The platform automates the entire workflow from data preprocessing to result analysis, supporting fair comparison across multiple representation-optimizer combinations. Furthermore, we designed a custom reaction rep... [more]

The growing penetration of renewable energy sources and power-to-hydrogen (P2H) systems demands high-fidelity, large-scale optimization frameworks that capture the nonlinear physics of both AC power flow and hydrogen thermodynamics. However, existing approaches rely on DC approximations and simplified electrolyzer models, neglecting critical operational constraints. As a result, accurately modeling such systems leads to large-scale nonlinear programs that are computationally intractable for conventional CPU-based solvers. This motivates the need for scalable optimization frameworks capable of handling both physical fidelity and computational complexity. This paper proposes a fully GPU-native framework for solving large-scale multi-period AC optimal power flow (AC-OPF) problems with integrated power-to-hydrogen systems. High-fidelity thermodynamic models of hydrogen production, compression, cooling, and storage are coupled with AC power flow constraints, resulting in large-scale nonline... [more]

This work explores symbolic regression (SR) as an interpretable surrogate modelling approach for the multi-objective optimisation of pressure swing adsorption (PSA) systems for CO2 capture. A first-principle model was used as a virtual plant to generate synthetic datasets covering the operating space defined by cycle step durations. Two surrogate frameworks were developed and compared: SR models derived through evolutionary search and deep neural networks (DNNs) trained via Hyperband-based tuning. Both surrogates were used as simulation models within an optimisation procedure based on a particle swarm optimisation (PSO) algorithm to maximise CO2 purity and recovery. While DNNs achieved the lowest prediction errors (MSE ˜ 10-6), the SR surrogates provided compact analytical representations and significantly faster optimisation. The SR framework yielded a denser and more diverse Pareto front (4345 vs 508 points). It was about 34 times faster (38.6 s vs 1331 s), confirming its efficiency... [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]

Ammonia has received in recent years significant attention as potential carbon free fuel. However, its combustion properties limit its direct application for both providing heat and in power generation through gas turbines. Ammonia cracking is one potential solution to circumvent the problem by producing hydrogen. When using the ammonia in gas turbines, it is possible to heat integrate the endothermic decomposition reaction with the exhaust gas from the gas turbine. Thermodynamic and kinetic limitations have however a major impact on the achievable ammonia conversion. Based on the consideration of these limitations, this paper presents a detailed investigation of key design parameters affecting the overall process efficiency utilizing both an equilibrium reactor model and a reactor model based on detailed kinetics and heat transfer. Ammonia decomposition should occur at sufficiently high pressure to avoid a) the com-pression energy demand for achieving the pressure of the combustion ch... [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]