The accumulation of automotive plastic waste poses a growing environmental threat; while recycling has the potential to address this, its use remains limited by the complexity of the materials used in vehicle components. Specifically, the presence of mixtures of polypropylene (PP), polyethylene (PE), and polyoxymethylene (POM) in the materials makes mechanical recycling challenging due to the difficulty of separation. To address the inefficiency of current end-of-life management, we present a systematic computational framework integrating computer-aided molecular and process design (CAMPD) with technoeconomic assessment (TEA) and life cycle analysis (LCA) to design a solvent-based recycling process capable of producing near-virgin quality resins. This framework involves utilizing the SAFT-?? Mie equation of state to predict thermodynamic properties and employing nonlinear programming (NLP) to perform process optimization. From an evaluation of 875 solvent candidates, we identify 72 fea... [more]

Gallium is a critical material with increasing demand driven by compound semiconductors such as gallium nitride (GaN) and gallium arsenide (GaAs) used in power electronics and optoelectronics and a highly concentrated supply chain, with 98 % of refined production occurring in China. While recycling remains limited, GaAs semiconductor manufacturing generates wastewater that can contain gallium concentrations ranging from 1-35 mg·L?¹, representing an underutilized secondary resource. This study evaluates the technical and economic feasibility of recovering gallium from GaAs semiconductor wastewater across an input range of 10-100 m³·d?¹ using process modelling and a techno-economic analysis comparing two alternative separation routes: ion exchange (IX) and solvent extraction (SX). Using a real-world industrial wastewater composition, IX achieves a higher overall recovery than single-stage SX (80.40 % vs. 62.54 %), which translates into consistently lower levelized costs of gallium. The r... [more]

The wine industry generates large volumes of organic effluents, whose inadequate management poses significant environmental challenges but also offers opportunities for resource recovery. In this work, an integrated biorefinery scheme for the valorization of winery effluents is proposed and evaluated through steady-state simulation in Aspen Plus®. The biorefinery converts winery wastewater into a portfolio of value-added chemicals and biofuels, including levulinic acid, propylene glycol, formic acid, light gases, naphtha, sustainable aviation fuel, green diesel, and bioethanol, while enabling water recovery and carbon dioxide management. Two alternative CO2 capture routes are analyzed and compared: a conventional CaO-based carbonation-calcination process and an innovative absorption system using deep eutectic solvents (DES), specifically choline chloride-urea. Technical performance is assessed through chemical oxygen demand (COD) removal, recovery, conversion, yield, and product mass r... [more]

The increasing popularity of electric vehicles (EVs) leads to an expected rise in the quantity of end-of-life lithium-ion batteries (LIBs) that require efficient management. This paper presents a State Task Network (STN) based optimization model to analyze and optimize the supply chain for LIBs, allowing for the selection of optimal processing routes, facility locations, capacities and reintegration of recovered materials, as well as analyzing the possible trade-offs between different end-of-life management strategies. Based on available data from the literature, the model is demonstrated with the LIB supply chain considering both primary production and different end-of-life strategies for spent LIBs (recycling and reuse). The case study reveals that mechanical pretreatment followed by hydrometallurgical recycling is the optimal pathway and it outperforms the linear supply chain in both costs and emissions. The cost optimal solution opts for more centralized collection and disassembly,... [more]

The transition from a linear, fossil-based polymer economy to a circular bio-economy is critical for mitigating resource depletion and greenhouse gas emissions. This study provides a rigorous comparison of two biomass-to-plastic pathways: a biochemical route (PLA via enzymatic hydrolysis) and a thermochemical route (bio-PE via gasification and MTO). Based on Aspen Plus simulations and a "lifetime-adjusted" lifecycle assessment framework, we evaluate the environmental performance of these routes in the transition from linear to circular systems. Unlike standard "cut-off" methods, the lifetime-adjusted model accounts for virgin make-up and molecular retention across multiple recycling cycles. Results indicate that at current 15% recycling rates, PLA exhibits the lowest global warming potential due to significant biogenic carbon sequestration. However, as recycling rates reach 75%, process efficiency becomes the dominant factor; the precise biochemical recycling of PLA continues to outper... [more]

This work presents a hybrid modelling approach for a downdraft sewage sludge gasifier within the Shit2Power (S2P) process. The gasifier is represented in CHEMCAD NXT by a series of four standard reactors that combine stoichiometric and equilibrium models with a data-driven correction step to account for deviations from ideal Gibbs equilibrium. Reaction conversions in the correction reactor are fitted to experimental synthesis gas compositions reported by Werle (2014) [7] for 30 operating points with varying equivalence ratios and reactor inlet air temperatures. The calibrated hybrid reactor model is evaluated against these data and shows conservative agreement for the combustible gas components of the synthesis gas. To overcome the limitations of linear interpolation between fitted operating points, several machine learning approaches are evaluated to predict the reaction conversions, and boosted neural networks are selected as a compromise between prediction accuracy and smooth behavi... [more]

An integrated multiproduct facility for the valorization of apple pomace in the green production of high value-added chemicals such as phenolic compounds and pectin, bioethanol, as well as apple juice, was optimally designed. The process includes green extraction technologies relying on subcritical water extraction and ethanol produced on-site through fermentation of residues. Two scenarios were evaluated: one based on purchasing the ethanol and another with on-site ethanol production. The units of the process were modeled using first principles. The process superstructure was formulated as a mixed-integer nonlinear programming problem, solved by decomposition. Investment and production costs of both alternatives were similar, with unit production requirements ranging from 1.09 €/L to 1.13 €/L. The discounted payback periods were 6.7 years and 6.4 years for the on-site ethanol production and purchased ethanol scenarios, respectively, while the internal rates of return were 36.4 and 37.... [more]

The utilization of low-quality vegetable oils as raw materials helps to reduce the production costs of biodiesel. Waste cooking oils are examples of this type of raw material, having a high content of free fatty acids. While biodiesel is a sustainable alternative to fossil fuels, conventional production methods face challenges due to low reaction rates and high energy demands. This study investigates pathways for biodiesel production from waste cooking oil using process intensification technologies in combination with biowaste-derived heterogeneous catalyst. Conventional and intensified (reactive distillation-based) processes are compared using Aspen Plus simulations as an analysis tool, focusing on the production of biodiesel from waste cooking oil (WCO) using CaO as a catalyst. According to the results, the conventional method at 60°C and 6:1 methanol-oil ratio achieves 95% conversion but suffers from high methanol use and long reaction times (65 min). The intensified process at 65-7... [more]

Conventional urea production is a centralized and fossilintensive process associated with significant greenhousegas (GHG) emissions and limited flexibility for deep decarbonization. As an alternative, the Integrated COnversion of NItrate and Carbonate steams (ICONIC) project is developing innovative electrochemical urea (eurea), via the co-electroreduction of nitrogen and carbon sources using renewable power. While recent research advances in electrocatalysis have demonstrated promising Faradaic efficiencies (FE) toward urea, the design of electrochemical systems involves inherent tradeoffs between key performance indicators (KPIs) such as current density, cell voltage, and FE. Crucially, the implications of electrolyzerlevel performance on plantlevel economics and environmental impacts remain poorly understood. To address this gap, we integrate process modelling with technoeconomic and lifecycle assessment (TEA-LCA) to evaluate the trade-offs of KPIs from a process systems per... [more]

Plastic waste represents an abundant and underutilized resource that can be converted into valuable products through microwave-assisted pyrolysis. In this research, a novel microwave-assisted processing plant that converts mixed plastic waste to olefins and aromatics is developed and simulated on Aspen Plus (v.14) guided by laboratory-scale experimental data. The experimental results show that at a bulk temperature of 400°C and ambient pressure, approximately 95% of a solid waste plastic feed comprised of equal portions of polypropylene and polyethylene is converted to gases, with nearly two-thirds of the resulting effluent gas composed of olefins. Simulation results show that 2889.1 kg/h propylene, 2088.0 kg/h ethylene and 96.3 kg/h aromatics (benzene and toluene) are produced as main products from 8000 kg/h of mixed plastic feed. High-purity propane and ethane streams were also recovered and sold as byproducts. A technoeconomic analysis is subsequently conducted, revealing that the p... [more]

The transition toward circular economy principles in water treatment requires advanced process systems engineering tools to evaluate the trade-offs between environmental sustainability and economic viability, particularly for energy-intensive Zero Liquid Discharge (ZLD) systems. While classic ZLD systems treat concentrated brine as waste, circular ZLD (CZLD) systems incorporate salt recovery technologies that generate marketable salt product. This study presents a comprehensive technoeconomic assessment framework for CZLD systems integrated with renewable energy. The framework is developed to evaluate different CZLD configurations that generate saleable sodium chloride. The assessment methodology integrates solar photovoltaic systems with increasing capacities (100-1400 kW) to analyze renewable energy penetration and energy storage requirements. The renewable energy integration model incorporates hierarchical energy dispatch algorithms prioritizing direct solar utilization, battery sto... [more]

The escalating urgency to address climate change has driven carbon capture (CC) technologies into the spotlight, particularly for large-scale emitters, which benefit from economies of scale. However, small-scale emitters account for a significant share of CO2 emissions, yet such applications remain largely overlooked in the literature. While CC cost is often used as a key performance indicator (KPI) for CC technologies, the lack of standardized cost estimation methods leads to inconsistencies, complicating comparisons, and hindering the deployment of CC systems. This study addresses these challenges by developing flexible short-cut correlations for selected CC technologies, providing estimates of the total equipment cost (TEC) and energy consumption specific to small-scale applications across various CO2 inlet concentrations (mol%) and capture scales (10 – 100 kt/y). The flexibility of the correlations enables the integration of various cost estimation methods available in the literatu... [more]

Job Safety Analysis (JSA) is critical for proactively identifying workplace hazards, assessing their potential consequences, and implementing effective control measures. However, traditional JSA methods can be inefficient and prone to errors, particularly in complex industrial environments. This paper introduces AutoJSA, a knowledge-enhanced framework that leverages large language models (LLMs) to automate and optimize the JSA process. We collected 73 high-quality JSA reports from a chemical engineering company and divided the JSA workflow into three key tasks: hazard identification, consequence identification, and control measure generation. Two approaches - fine-tuning and retrieval-augmented generation (RAG) - were employed on a base LLM (GLM-4-9B-Chat) to adapt it for these domain-specific tasks. Experimental results demonstrate that both fine-tuning and RAG significantly improve task performance relative to the unmodified model, with fine-tuning generally providing larger gains. W... [more]

Graph neural networks (GNNs) have proven state-of-the-art performance in molecular property prediction tasks. However, a significant challenge with GNNs is the reliability of their predictions, particularly in critical domains where quantifying model confidence is essential. Therefore, assessing uncertainty in GNN predictions is crucial to improving their robustness. Existing uncertainty quantification methods, such as Deep ensembles and Monte Carlo Dropout, have been applied to GNNs with some success, but these methods are limited to approximate the full posterior distribution. In this work, we propose a novel approach for scalable uncertainty quantification in molecular property prediction using Stochastic Gradient Hamiltonian Monte Carlo (SGHMC). Additionally, we utilize a cyclical learning rate to facilitate sampling from multiple posterior modes which improves posterior exploration within a single training round. Moreover, we compare the proposed methods with Monte Carlo Dropout a... [more]

Distillation processes account for a substantial share of the industrial energy demand. Yet, these energy requirements can be reduced by a variety of energy integration methods, including various forms of direct heat integration, multi-effect distillation, thermal coupling and vapor recompression. Consequently, these intensification methods should be evaluated quantitatively in comparison to each other for individual separation tasks, instead of benchmarking single options with conventional sequences or relying on simplified heuristics. In order to overcome the computational burden of a broad assessment of a large number of process alternatives, a computationally-efficient framework for the energetic and economic evaluation of such energy integrated distillation processes is presented, which builds on thermodynamically-sound shortcut models that do not rely on constant relative volatility and constant molar overflow assumptions.

A new methodology for the assessment of the performance loss in catalytic reactors due to deactivation was developed and applied to fixed- and fluidized-bed CO methanation, with catalyst subject to coking. The methodology is based on the solution of heat and mass balances, by decoupling the reactor and deactivation dynamics. This is possible by using consecutive 1D, steady-state calculations for the characterization of the reactor performance. In this way, the progressively lower values of catalyst activity along the time on stream are computed with the integration of a dedicated dynamic model. This method has shown promising results in the characterization of the loss of performance of the reactor over time. The model correctly describes a progressive deactivation of the catalyst in fixed-bed reactors, while it shows that the decrease in activity is sudden for the whole reactor volume in fluidized bed reactors and occurs after a critical time-on-stream. Besides, it was observed that t... [more]

Hybrid modelling is widely employed in chemical engineering to generate highly accurate predictions. Such an approach merges first-principle modelling with machine learning techniques to identify and model the epistemic uncertainty from experimental data. Despite its advantages, this still requires cross-domain competencies that are difficult to find in the chemical industry and high human involvement. The possibility of automating the identification and training model would be significantly beneficial for the widespread adoption of hybrid modelling methodology within the chemical industry. This work presents a novel algorithm for the automatic identification of hybrid models (HMs) starting from the first-principle representation of the system, described by differential equation sets. The methodology formulates the problem as mixed-integer programming, identifying the equation running under uncertainty, identifying the machine learning model hyperparameters, and training the latter. Th... [more]

This study proposes a model-based approach utilizing a hybrid population balance model (PBM) to optimize temperature profiles for minimizing agglomeration and enhancing crystal growth. The PBM incorporates key mechanisms—nucleation, growth, dissolution, agglomeration, and deagglomeration—and is applied to the crystallization of an industrial active pharmaceutical ingredient (API), Compound K. Parameters were estimated through prior design of experiments (DoE) and refined via additional thermocycle experiments. In-silico DoE simulations demonstrate that the hybrid PBM outperforms traditional methods in assessing process performance under agglomeration-prone conditions. Results confirm that thermocycles effectively reduce agglomeration and promote bulk crystal formation, though their efficiency plateaus beyond a certain cycle number. This model-based approach provides a more robust strategy for agglomeration control compared to conventional methods, offering valuable insights for industr... [more]

Mathematical modeling is essential for the effective control of many chemical engineering processes, including crystallization. However, most existing crystallization models used in industry and academia assume ideal mixing. As a result, the unclear effects of imperfect mixing on crystallization, reported in experimental studies, remain largely unexplained. In this work we aim to address this gap in understanding by examining antisolvent crystallization processes on a general theoretical level, using a novel dimensionless model. To address the impact of mixing on crystallization, we employ the Engulfment model coupled with a population balance, and we nondimensionalize the model equations. Using this model, we explore the dependence of the mean particle size on the homogenization rate, represented by the Damköhler number for crystallization. Moreover, we study the impact of mixing at various values of the model's kinetic parameters to simulate difference in properties of individual pro... [more]

As pharmaceutical manufacturing is transitioning from traditional batch to continuous manufacturing (CM), there is a lack of tools for CM design and development, which can integrate drug substance and drug product unit operations for overall evaluation. Recently, a Python-based PharmaPy framework was proposed to advance the design, simulation, and analysis of continuous pharmaceutical processes. However, the initial library of models only addressed upstream drug substance processing. In this work, new capabilities, including drug product unit operations such as feeder, blender, and tablet press, have been added to the PharmaPy framework, enabling end-to-end study and optimizing the effects of material properties and process conditions on solid oral dosage products. The platform supports computational efficiency and model accuracy by allowing the development of different mechanistic and semi-mechanistic models. Sensitivity analysis is performed on the integrated end-to-end simulator to... [more]

Mechanistic population balance modeling (PBM) has advanced the design of pharmaceutical crystallization processes, enabling the production of active pharmaceutical ingredient (API) crystals with desired critical quality attributes (CQAs), such as purity and crystal size distribution. However, PBM development can sometimes be resource-intensive, requiring extensive design of experiments (DoE) and high-quality process data, making it impractical under fast-paced industrial development timelines. This study proposes a machine learning (ML)-based workflow for developing ‘fit-for-purpose’ digital twins of crystallization processes, leveraging industrially available DoE data to link operating conditions with CQAs. Validated on industrial data for a commercial API with complex crystallization challenges, the workflow efficiently identifies optimal operating conditions, demonstrating the potential of data-driven digital twins to accelerate the development of pharmaceutical processes.

Tableting is a dry granulation process for compacting powder blends into tablets. In this process, a blend of active pharmaceutical ingredients (APIs) and excipients are fed into the hopper of a rotary tablet press via feeders. Inside the tablet press, rotating feed frame paddle wheels fill powder into dies, with tablet mass adjusted by the lower punch position during the die filling process. Pre-compression rolls press air out of the die, while main compression rolls apply the force necessary for compacting the powder into tablets. In this paper, process variables such as feeder screw speeds, feed frame impeller speed, lower punch position during die filling, and punch distance during main compression have been systematically varied. Corresponding responses, including pre-compression force, ejection force, and tablet porosity have been evaluated to optimize the tableting process. After implementing an open platform communications unified architecture (OPC UA) interface, process variab... [more]

Crystallization is a relevant process in the pharmaceutical industry for product purification and particle production. An efficient crystallization is characterized by crystals produced with the desired attributes. Therefore, modeling this process is a key point to achieve this goal. In this sense, the objective of this work is to propose a hybrid model to describe paracetamol dissolution in ethanol. The universal differential equations methodology is considered in the development of this model, using a neural network to predict the dissolution rate combined with the population balance equations to calculate the moments of the crystal size distribution (CSD) and the concentration. The model was developed using experimental batches. The dataset is composed of concentration measurements obtained using attenuated total reflectance-Fourier transform infrared (ATR-FTIR). The objective function of the optimization problem is to minimize the relative absolute difference between the experiment... [more]

In 2023, the International Council for Harmonisation (ICH) guideline for the development, implementation, and lifecycle management of pharmaceutical continuous manufacturing (PCM), was implemented in Europe. It promotes quality-by-design (QbD) and quality by control (QbC) strategies as well as the appropriate use of mathematical modelling. This development urges a harmonizing understanding across academia and industry for adoption of interpretable models instead of black-box models for advanced control strategies such as model predictive control (MPC), especially when applied in Good Manufacturing Practice (GMP) regulated areas. To this end, we first propose a comprehensive model development using Dynamic Mode Decomposition with Control (DMDc)to represent complex dynamics in a lower-dimensional space, disambiguating between underlying dynamics and actuation effects. Using data from a digital twin of PCM, our model demonstrates low computational complexity while effectively capturing no... [more]

The environmental impacts of pharmaceutical production underscore the need for comprehensive life cycle assessments (LCAs). Offshoring manufacturing, a common cost-saving strategy in the pharmaceutical industry, increases supply chain complexity and reliance on countries like India and China for active pharmaceutical ingredients (APIs). The COVID-19 pandemic exposed Europe’s vulnerability to global crises, prompting initiatives such as the French government’s re-industrialization plan to relocate the production of fifty critical drugs. Paracetamol production has been prioritized, with recent shortages highlighting the urgency to address supply chain risks while considering environmental impacts. This study uses process engineering to generate life cycle inventory (LCI) data for paracetamol production, offering an eco-design perspective. Aspen Plus was employed to model the API manufacturing process, integrating mass and energy balances to address the scarcity of LCI data. The results h... [more]