The design of cyclic adsorption processes is computationally expensive as it involves screening many process designs, each of which involve a time-consuming simulation to reach cyclic steady state. In this work, we demonstrate how differentiable programming can be used to accelerate both the simulation and attainment of cyclic steady state for a four-step pressure vacuum swing adsorption (PVSA) process to concentrate carbon dioxide from flue gas. A mechanistic one-dimensional dynamic adsorption model was implemented in JAX, enabling automatic differentiation and just-in-time compilation for efficient solution and accurate sensitivity evaluation. The latter was exploited to implement a Newton-based direct determination method for accelerated convergence to cyclic steady state, avoiding repeated cycle simulations. Across 4096 designs sampled in a six-dimensional design space, the direct determination method converged in an average of 4.6 iterations, compared to 145 cycles required by suc... [more]

Environmental concerns caused by CO2 emissions has attracted much attention by researchers worldwide. CO2 can be captured from large single sources such as power plants to reduce the CO2 emission. Solvent-based post-combustion carbon capture (PCC) process for large scale biomass-fired power plant could achieve negative carbon emission. However, capture level is commonly set at 90% in many studies. The small fraction of residual CO2 is still a large amount due to the high flue gas flowrate. In this study, a piperazine-based PCC process at 95% capture level for biomass-fired power plant was studied. The process was simulated in Aspen Plus® V11, validated and scaled up. The energy performance results showed that when the capture level is increased to 95%, the reboiler duty rises to 4.07 GJ/tCO2, corresponding to an increase of approximately 13.7% compared to the 90% case. This additional regeneration energy demand is offset by the reduction in residual CO2 emissions from flue gas or 0.23... [more]

The global transition toward sustainable energy has intensified research into biofuels, with bioprocess optimization playing a central role in achieving decarbonization goals. Biobutanol, in particular, is a high-value molecule for sustainable fuel applications due to its superior energy density and compatibility with existing infrastructure. However, model-based optimization of its production is hindered by traditional semi-structured kinetic models that often suffer from limited predictive robustness. To address this challenge, within this study we developed a hybrid modeling framework for Clostridium saccharoperbutylacetonicum that integrates mechanistic mass-balance equations with Gaussian Processes (GPs) aiming to describe the biobutanol formation rate. Here, we investigate the effect of data normalization techniques on hybrid model's prediction capabilities comparing min-max normalization, z-score normalization, and no transformation. For each data treatment strategy, 8, 000 hybr... [more]

The interdependence among energy, water, food, and land sectors has been addressed through the concept of Energy-Water-Food-Land nexus (EWFLN), where interconnections between different sectors generate complex feedback loops. In the field of EWFLN, transitioning from deterministic to stochastic approach is considered as the natural choice for policy makers. Working with a large number of uncertain parameters can make a stochastic system complex. Therefore, Identification of the significant uncertain parameters in the system would create a more acceptable model before transforming from a deterministic to a stochastic approach. This study incorporates uncertainty prioritisation in the mathematical model for the optimisation of EWFLN. The specific objectives of this study include creating a mathematical model, determining and prioritising uncertain parameters, and prescribing appropriate policy recommendations. This study points out clearly which specific parameters should be taken into c... [more]

Polystyrene (PS) recycling remains limited despite large waste volumes, largely because many routes struggle to recover high-value chemicals at scale. This work develops a full process concept that upgrades PS through low-pressure hydrothermal processing (HTP) and directs the resulting aromatic oil to separation and downstream conversion. The superstructure includes HTP at 30 bar and 350°C, three distillation columns for toluene/ethylbenzene/styrene splits, ethylbenzene dehydrogenation to boost styrene yield, steam reforming of the heavier C9+ fraction to syngas, and an ICI-type (Imperial Chemical Industries) methanol loop with inter-bed quench. The integrated flowsheet was simulated in Aspen Plus V14 using a consistent NRTL-RK (Non-Random Two-Liquid-Redlich-Kwong) property framework. Deep-vacuum operation (˜100-400 mbar) was applied where needed to limit styrene polymerization. Sensitivity and optimization focused on meeting a syngas stoichiometric number near 2; the best case occurre... [more]

Direct air capture is gaining significant interest due to its potential to achieve carbon-negative emissions. With the aim to reduce the energy consumption and in line with the electrification of chemical processes, the absorption direct air capture system is integrated into a bipolar membrane electrodialysis cell stack for solvent regeneration and carbon dioxide release. The scheme solution is characterized by carbon dioxide bubbles inside the cell reducing efficiency so that other regeneration schemes have been proposed in the literature. A direct comparison of those is missing in the existing state of the art: the present work wants to fill this gap. In addition to the above process, the reaction of the rich solution with a weak organic acid and the use of both nanofiltration and reverse osmosis membranes are considered in other process schemes. The three case studies are modelled in Aspen Plus software with the aim to compare the energy consumption and total cost while the environm... [more]

The rapid integration of renewable energy sources is increasing the volatility and non-stationarity of modern power systems, posing significant challenges for data-driven forecasting models. In particular, concept drift and uncertainty in exogenous inputs such as weather forecasts can severely degrade predictive performance over time. This work proposes a lightweight two-layer forecasting framework that decouples prediction from adaptation. A traditional offline regression model is augmented by an online meta-learner that continuously generates adaptive meta-features, enabling the system to respond to structural changes and noisy inputs without repeated retraining. The framework is evaluated on two real-world case studies. First, concept drift is addressed in nuclear power production forecasting, where abrupt and gradual capacity changes are inferred through an online meta-learner. Second, feature uncertainty is mitigated in day-ahead solar production forecasting by correcting noisy we... [more]

The present work aims to demonstrate the synergy achieved through the integration of biomass gasification with a direct air capture (DAC) system to maximize overall CO2 removal capacity, while simultaneously converting waste into value-added products (hydrogen) and supplying the energy required for DAC operation (BG-H2P-DAC). The proposed configuration is modeled using Aspen Plus to investigate the synergistic interactions and key performance indicators of the BG-H2P-DAC system. Parametric analyses are conducted by varying gasification temperature, air inlet flow rate, and amine concentration and flow rate. The results indicate that increasing the monoethanolamine (MEA) concentration from 10% to 40% leads to a gradual decline in CO2 capture efficiency, accompanied by a reduction in CO2 slip. The system achieves a net specific electricity consumption of 0.0293 MWh/t CO2, confirming that the electricity generated from the integrated steam power cycle is sufficient to fully offset the ele... [more]

Accurately predicting chemical reaction yields can accelerate reaction optimization by prioritizing promising conditions; however, the complexity of multicomponent reactions and the limited availability of high-quality datasets remain significant challenges. While machine learning has achieved substantial progress in molecular property prediction, reaction-level modeling requires representations that capture three-dimensional structures, intercomponent interactions, and mechanistically critical features. In this study, we investigate the effective extension of molecular pretraining to reaction yield prediction from two complementary perspectives. First, we apply a reaction-aware architectural design based on molecular representation models pretrained via partial denoising to multicomponent reactions. Each reaction component is encoded as a three-dimensional stereoisomer embedding and concatenated into a reaction-level representation, with multi-head attention modeling intercomponent de... [more]

The availability of temperature-dependent physicochemical property data forms the cornerstone of process simulation, optimization, and sustainable molecular and product design. However, a critical data gap persists, as experimental measurements are accessible for only a small subset of known chemicals. This renders experimental characterization resource-prohibitive, often compelling reliance on empirical estimation methods. Moreover, although many models offer single-point predictions at fixed temperatures, accurately modeling continuous temperature-dependent behavior remains challenging. Conventional methods frequently overlook intermediate variations, resulting in limited extrapolation capability. To overcome these limitations, we introduce a mechanism-guided hybrid modeling framework that integrates physical insights into data-driven models. This framework is built on two strategies. Strategy ? targets trend correction by generating a continuous representation from discrete single-p... [more]

A recurrent neural network (RNN) model for a CO2 methanation reactor was developed based on synthetic data generated from a validated mechanistic model of the same unit. The model was used to predict the main properties of the reactor - methane productivity and hotspot temperature - during a dynamic operation of the unit. The dynamic profile of feedstock availability was simulated taking into account the H2 flow that can be produced from PV-powered water electrolysis using solar irradiation profiles over a year in Milan, Italy. The dataset therefore consists of 366 data instances (one for each day), each composed of one datapoint per minute of sunlight. The best agreement between the predictions from the RNN and the target output values from the mechanistic model was found using a shallow RNN of 20 hidden-layer neurons, trained with a batch size of 10 and an 80/20 training-testing split. This showed that RNNs can constitute a reliable tool for dynamic surrogate modelling of energy conv... [more]

As the demand of freshwater continuous to rise, the water desalination can act as prominent solution to address the global water scarcity. However, significant environmental concerns arise as the process is mainly powered by fossil fuel, which contributes to greenhouse gas emissions and thus leading to global warming. This research intends to explore the potential of renewable energy sources to effectively reduce freshwater production cost. Specifically, it intends to estimate the power generated and freshwater production cost of a multi-effect distillation (MED) desalination process, powered by solar and wind energy sources in addition to a comparison against the performance of an MED process powered by fossil fuel based conventional steam boiler. The comparison of energy efficiency and freshwater production cost is conducted in two different UK locations: Wick in the north of Scotland and Watchet in the south of England. MED process model with both wind power and solar energy models... [more]

The conceptual design and performance of a novel Methylcyclohexane-Toluene-Hydrogen based chemical heat pump was studied using steady state simulations. The distillation operating parameters of the chemical heat pump were optimized to maximize the Coefficient of Performance based on heat quantity (COP) and its corresponding Coefficient of Performance based on electric work input (COPW) was calculated. The best operating temperature ranges of the endothermic and exothermic reactor are 200°C-225°C and 250°C-275°C respectively. An endothermic temperature of 200°C and an exothermic temperature of 250°C results in a COP of 0.1357 and a COPW of 13.3. By integrating this chemical heat pump with a vapor compression heat pump COP increased to 0.1445 while COPW reduced to 4.9.

Model adaptation to production environment is critical for reliable Machine Learning Operations (MLOps), less attention is paid to developing systematic framework for updating the ML models when they fail under drift. This paper compares the transfer learning enabled model update strategies including ensemble transfer learning (ETL), all-layers transfer learning (ALTL), and last-layer transfer learning (LLTL) for updating the failed feedforward artificial neural network (ANN) model. The flue gas differential pressure across the air pre-heater unit installed in a 660 MW thermal power plant is analyzed as a case study since it mimics the batch processes due to load cycling in the power plant. Updating the failed ANN model by three transfer learning techniques reveals that ETL provides relatively higher predictive accuracy for the batch size of 5 days than those of LLTL and ALTL. However, ALTL is found to be suitable for effective update of the model trained on large batch size (8 days).... [more]

Brazed aluminum heat exchangers (BAHX) are key cryogenic equipment, but their simulation is sensitive to phase change modelling. This work benchmarks ProSec, CO-ProSec Reaction, and Aspen EDR on an ethylene-chiller BAHX using ethane in thermosiphon mode. ProSec's interpolation scheme of tabulated data is validated against CO-ProSec Reaction's approach that employs a thermodynamic model for the evaluation of thermodynamic and physical properties; an initial 25% pressure drop gap is traced to different void-fraction models (slip vs homogeneous). Against Aspen EDR, default duty/quality are ~15% higher; differences are mainly due to nucleate-boiling treatment and distributor pressure drop modelling. Harmonizing options reduces duty mismatch to ~5%.

Bioprocess modelling, optimisation and scale-up are central components for improving sustainable manufacturing within pharmaceutical and chemical industries. However, developing accurate bioprocess digital twins remains a challenging process. Conventional mechanistic models are difficult to construct because of limited mechanistic understanding and large complexity of cellular metabolisms. While data-driven models have gained popularity, they often require large amounts of experimental data that is often time consuming to obtain and lack any quantitative description of the process. Hybrid modelling methods have emerged as promising alternatives however fail to provide physical insight to the root cause of model error. This work therefore presents a promising solution by developing a data-efficient symbolic regression (SR) based framework to enable the automated discovery of interpretable bioprocess models. A universal kinetic model backbone was used to capture overall process behaviour... [more]

The objective of the present work is to optimize the simulation of an electrodialysis reactor for the desalination and regeneration of multi-ionic wastewater with high salt contents and conductivities, within the framework in the Sustainable Development Goal 6 (clean water and sanitation) and remarking the Electrodialysis (ED) as a highly energy-efficient and sustainable technology. The mathematical modelling has been carried out by using a semiempirical model that involves an algebraic system of differential equations, including mass and charge balances (taking into account the ions present in the wastewater: Na?, Ca²?, Mg²?, Cl?, SO4²?, and HCO3?), and the total electrodialysis stack voltage considering ohmic drops (in the dilute and concentrate compartments), the potential of membrane in each cell pair, and the electrode potentials. In the simulation process, different theoretical and experimental parameters are necessary such as number of cells, membrane working areas, efficiency,... [more]

Geologic hydrogen has emerged as a promising low-carbon energy vector, but its sustainable recovery requires effective stimulation and production strategies. This study presents an integrated process-modeling framework for evaluating hydrogen extraction from hydraulically stimulated reservoirs. The framework combines fracture propagation, damage evolution, Darcy-scale multiphase flow, permeability-aperture dynamics, and a dual-porosity dual-permeability (DPDP) representation to simulate hydrogen production in fractured source rock systems. In addition to production dynamics, the framework tracks operational water and energy inputs and incorporates a simplified reaction-extent formulation to represent hydrogen generation under data-limited conditions. The model was benchmarked against published shale gas production results to evaluate its ability to reproduce fracture-controlled production behavior and was subsequently applied to a representative multi-well hydrogen development scenario... [more]

Reliable kinetic models are essential for the design, optimisation and operation of methanol synthesis reactors in power-to-X applications. However, parameter estimation is frequently performed without prior assessment of parametric identifiability or the information content of experimental conditions, often resulting in poorly constrained parameters and inefficient experimental campaigns. This study introduces a systematic, pre-calibration, information-driven framework for identifiability analysis and experimental design. The framework integrates local sensitivity analysis, structural and practical identifiability metrics and a Sequential Information-Driven Experimental Selection (SIDeS) strategy to guide experiment selection prior to parameter estimation. The methodology is applied to four literature kinetic models for methanol synthesis, spanning varying levels of mechanistic detail. A Sobol-sampled design space is first screened for feasibility and local information content, follow... [more]

In this work, artificial neural networks are used to represent advanced oxidation processes, such as electrochemical oxidation, electro-Fenton, and photoelectro-Fenton, for the degradation of two dyes. The effect of treatment time, initial dye concentration, and current density on the degradation percentage is studied. Additionally, a network is developed to include discrete variables, such as treatment type and dye type, as input features, enabling it to predict the system's performance across different technologies and pollutants. Operating conditions are optimized using the universal ANN as a surrogate model and the adaptive differential evolution algorithm to maximize dye removal efficiency. According to the results, after optimizing the architecture of the artificial neural networks using Bayesian optimization, deviations of 3.9% or less are obtained for purple RL removal predictions, while for Green A, deviations of 8% or less are obtained. These models may serve as a basis for m... [more]

This work presents the dynamic optimization of glucose feed in cell cultivation considering the balance between productivity and impurity generation. We first developed a mechanistic model considering cell growth promotion by glucose and cell growth inhibition by osmolarity for a newly developed, high-productivity CHO-MK cell line. For model development, fed-batch cultivation experiments were conducted at a 250 mL scale under three different glucose feeding profiles. Results from a single-objective dynamic optimization, using the glucose feed profile as a design variable, were compared to those from multi-objective problem settings with varying weights assigned to productivity and final impurity concentrations. Simulation results suggested different glucose feed profiles depending on the priority given to the mAb and impurities, where the main difference was in the generated viable cell density profiles. Productivity-focused profiles employed a low-high-intermediate feeding strategy, i... [more]

In the field of biochemical process design, the accurate modeling of microbial growth is essential for the development and optimization of biological reactors used in the production of high-value compounds. Achieving this objective requires a detailed understanding of how environmental factors-such as pH and nutrient availability-influence microbial dynamics across the four distinct growth phases: lag, exponential, stationary, and death. Traditionally, reactor design relies heavily on the Monod model, which provides a simplified representation of microbial growth, focusing primarily on the exponential phase under constant operating conditions (1). However, this model presents substantial limitations when applied to dynamic environments where key parameters vary over time. To overcome these constraints, the present study proposes a data-driven modeling approach using a multilayer perceptron (MLP) artificial neural network for the prediction of microbial growth trajectories under varying... [more]

Magnetic Inductive Swing Adsorption (MISA) is a carbon dioxide capture process similar to Temperature Swing Adsorption that uses direct electromagnetic heating instead of classic heating systems for the regeneration step of the process. However, the lack of validated dynamic models hinders process optimization. This work introduces an open-source MISA model in the IDAES framework, incorporating Specific Absorption Rate (SAR) physics (SAR ? B²) to capture electromagnetic heating. Binary Sips isotherm parameters for Fe3O4@HKUST-1 were fitted to experimental data, achieving high statistical agreement (R2 > 0.996, RMSE < 0.022 mol/kg). Comprehensive validation was performed against adsorption isotherms, dynamic breakthrough curves, and desorption profiles. The model predicts breakthrough time with only 9% error and saturation time with 6% error. Crucially, the coupled thermal transport and SAR heating model capture temperature evolution during desorption within 5% error across all field st... [more]

Recent advances in computer-aided retrosynthesis (CAR), flow chemistry, and continuous manufacturing collectively offer new opportunities to enable environmentally sustainable development and manufacturing practices across the pharmaceutical development and manufacturing value chain. However, the implementation of these methods and technologies remains scattered and fragmented, preventing full realization of their potential to address one of the most urgent needs in the pharmaceutical and related sectors. This work introduces a holistic digital framework for the design and optimization of an end-to-end manufacturing process for paracetamol (acetaminophen). The framework integrates Green-by-Design synthetic and purification routes of the active pharmaceutical ingredient (API) aims to deliver cost efficiency and robust quality, safety, and environmental sustainability assurance. The approach integrates AI-driven CAR with plant wide modelling, Techno-Economic Analysis (TEA), and prospecti... [more]

Decisions taken throughout a bioprocess lifecycle are often guided by heuristic knowledge that is difficult to summarize and sort, scattered across heterogeneous tools and documents, and partly retained as tacit expert mental models alongside fragmented computational models. This fragmentation remains a central barrier to reproducibility, transparent provenance, and systematic reuse of prior learning across comparable development projects. In this paper, it is argued that a key missing link toward Bioprocessing 5.0 is the digitalization of FAIR knowledge through a Cognitive Digital Thread that couples semantic knowledge graphs with AI methods to connect experimental data, protocols, workflows, and decision rationale with mathematical models and digital twins in a machine-actionable and auditable manner. A digitalization roadmap is outlined as a sequence of capability stages-from local device and data integration, to reproducible workflow execution and metadata capture, to semantic know... [more]