Nitrogen-based fertilisers are pivotal for global food security, yet their production is a notable source of greenhouse gas emissions. Urea, a vital fertiliser with significant market presence—19% in Europe and 33% globally—is produced through an energy-demanding process reliant on fossil fuels. This study introduces a ’Green’ Urea plant concept, aimed for implementation in Ravenna, Italy, harnessing exclusively renewable energy sources to foster agricultural sustainability. With a production capacity of 1,300 tonnes per day, this facility neighbours Italy’s first carbon capture and storage (CCS) facility at Ravenna. The core of the proposed methodology is the synthesis of green ammonia. Seawater Reverse Osmosis-Polymer Electrolyte Membrane Electrolysis (SWRO-PEM) and Pressure Swing Adsorption (PSA) yield the necessary hydrogen and nitrogen feedstocks. An enhanced Haber-Bosch process utilising a Ru-based catalyst, facilitating lower operational conditions (500◦C and 100 bar) for the af... [more]

Nowadays, it is crucial to change daily habits to live in a more sustainable world. From an industrial point of view, the capture of CO2 is becoming more and more important in the chemical industry to reduce greenhouse gas emissions and its reuse can be an alternative to fossil resources. Another major challenge for future engineers is the significant increase in the use of renewable energy sources. In this perspective, a process allowing the synthesis of three different olefins from CO2 captured in industrial flue gases and using only wind energy is established. This process is separated into three major sections: water electrolysis, carbon dioxide reduction to produce methanol and methanol-to-olefins synthesis. The targeted production capacity is of 450 000 tonnes per year of olefins, which are considered to be ethylene, propylene and butylene. This process, which involves a complete flowsheet modelling is implemented with the Aspen Plus software. A heat integration is performed to i... [more]

To create useful products from carbon dioxide, electrochemical reduction is of the most promising approaches. Electrochemical reduction can use renewable energy to directly produce useful products such as formic acid, carbon monoxide, methanol or other C2 products. Specifically in Greece, methanol has been proven as a promising alternative for marine fuel, and it has been increasing in demand recently. As such, the proposed design is aimed to target this market. This paper will focus on the production of methanol using direct CO2 electro-reduction using Direct Air Capture (DAC) for the CO2 feed. A mathematical model of the electrolyser was created and implemented in Python. This model was then used alongside renewable energy production data from Open Power Systems [1] to optimise the total annualised cost with the constraint that the plant could only use renewable energy and must produce a minimum methanol flowrate. A combined stochastic search and derivative-free optimisation method w... [more]

The restructuring of the chemical industry towards the use of CO2 and intermittent, renewable energy sources poses a significant challenge for chemical engineers. Based on a systematic screening of current carbon-based chemical processes, we identify a promising combined reversible solid oxide cell (RSOC) and sorption-enhanced DME synthesis (SEDMES) process which produces dimethyl ether from captured CO2 and wind-generated electricity. Existing flowsheet alternatives are researched and a novel process design is proposed and simulated using Aspen Plus® and MATLAB®.
The optimization is divided into a design and a demand side management problem, solved by a genetic algorithm and the linear programming solver CPLEX, to determine the optimal operation and optimal production regime dependent on dynamic renewable electricity availability and price. The thermodynamic, economic, and ecological performance is assessed and compared to a selected fossil based state-of-the-art and biomass based st... [more]

The use of captured CO2 as a raw material is a quite old concept that has however received more and more attention recently. Indeed, carbon capture units are increasingly being developed as well as new technologies for the storage, the utilisation and the transformation of this captured CO2. This is driven by the increasing necessity to move towards more sustainable production processes and to mitigate greenhouse gases emissions.
The storage of CO2 in earth’s layers being a cost only technology, the alternative consisting in the production of novel chemical products or key substitutes to fossil-based chemicals seems attractive. In this perspective, two processes for dimethyl carbonate (DMC) production from captured CO2 are discussed. The selected pathways both differ from usual dimethyl carbonate units in the selected raw materials and in the choice of energy used. Both processes rely on the direct synthesis of DMC from methanol and carbon dioxide. Each implies the utilisation of a de... [more]

Vaccines are typically produced in large facilities to take advantage of economies of scale. However disease outbreaks are often local in nature and require flexible, small-scale production, especially in regions with poor infrastructure. In this work, mobile on-demand vaccine production is explored as a solution to future outbreaks. An mRNA vaccine process is scaled down to the size of two 20-foot shipping containers, so that 10,000 vaccine doses can be produced in one batch in less than 16 hours. The container is self-sufficient except for the regular resupply of water and electricity being able to produce 100 batches without resupply raw materials and consumables. The final cost per dose is estimated to be 25 e with a likely range between 4 to 45 e depending on dose size, raw material prices, and other underlying assumptions. The practicality of a container-based facility at the presented scale is demonstrated by two case studies.

Replacing fossil resources with green hydrogen in industrial production holds tremendous potential for greenhouse gas mitigation. The economic feasibility and greenhouse gas (GHG) mitigation of grid-based electrolytic hydrogen production is highly dependent on the time-variant price and carbon footprint of electricity. In the present contribution, we analyse the economic feasibility of transitioning key carbon-intensive industries, steelmaking, and ammonia production, to green electrolytic hydrogen. Also, we investigate the competitiveness of green electrolytic hydrogen with other environmentally sustainable hydrogen sources derived from biomethane, biogas, and natural gas (associated with carbon capture and storage). We perform process design for steelmaking, ammonia production, and biogas-based steam reforming in order to determine key performance indicators such as costs, conversion factors, and GHG emissions. In particular, we allow for dynamic operation of the industrial processes... [more]

In this work, a conceptual design is presented of a HVO/green diesel production unit with a processing capacity of 74 ton/h (500 000 ton/year) of vegetable oils and a production rate of 59 ton/h of diesel. Firstly, an extensive literature review has been conducted regarding the state-of-the-art techniques as well as process equipment, mechanisms of reaction and thermodynamical properties. A market analysis is also presented which estimates feedstock availability and target production rate. With this information, a preliminary Process Flow Diagram is proposed, along with explanations on the type of equipment used and its operating conditions. Process design and simulation has been performed using Aspen Plus®, while Aspen Custom Modeler® has been used to develop more accurate models where necessary. The present study concludes with an analysis of process flexibility, considerations for heat integration and an economic assessment.

This work proposes a conceptual process design of a production plant for hydrotreated vegetable oil (HVO). Palm oil is selected to be the most promising feedstock in terms of costs and chemical composition. Since UNIFAC is unable to correctly estimate the behavior of the liquid phase, an implementation of COSMO-RS is used as a more appropriate tool for the parameter estimation. As pre-treatment inorganic impurities in palm oil are removed with citric acid. A sulfur-free-Ni-catalyst embedded into a trickle bed reactor is applied for the conversion of palm oil to paraffinic fuel. Unit production costs of HVO of 0.85USD/kg (U.S.) and 0.91USD/kg (EU-27) are determined by using current palm oil prices. Those results are found to be marginally higher than costs for biodiesel production from palm oil. The blending capabilities of HVO with various diesel surrogates are calculated considering the DIN EN 590 standard.

Algal biomass production, mineralization, and chemical conversion as promising carbon dioxide utilization processes are compared with regard to economic as well as environmental factors. The production of the chemicals methanol, dimethyl ether, and dimethyl carbonate is selected as the most viable alternative among all options. The integrated production of the proposed chemicals is evaluated for a wide range of trade-offs between economic potential and environmental impact by applying multi-objective superstructure optimization. The results indicate that direct hydrogenation of CO2 to methanol with subsequent dehydration to dimethyl ether is on the verge of profitability (including capture cost) while achieving a positive net CO2 consumption of ca. 68% of supplied CO2 when direct and indirect emissions are accounted for; and 85% when only direct emissions are considered.

In this work, we propose a process to reduce CO2 emissions through its capture and utilization (CCU) as a raw material for producing valuable products in the chemical industry. As a case study, we design and evaluate the economic and environmental performances of a direct dimethyl ether (DME) synthesis from syngas plant reusing CO2 as a raw material. The decision making is carried out including all the design variables into a flowsheet superstructure, which is simulated and optimized to maximize the process profit. The optimum production of DME is 219.95 kt/year at 99.95% purity, with a profit of $51.01 million/year and emitting 0.784 kg CO2-eq/kg DME produced. After heat integration implementation, the profit is raised to $58.68 million/year and emissions are reduced to 0.510 kg CO2-eq/kg DME, being the latter a 61.4% lower than the one associated to the classic DME production. The financial risk associated with the post heat integration process is at 15.4%, while considering a 5% ris... [more]

This is the source code written in MATLAB for the stochastic, agent-based model for naive CD8+ T cell recirculation dynamics in mice. The model simulates the migration of naive CD8+ T cells between the blood and major lymphoid tissues in the body for 47 hours post i.v. transfer. It is also capable of predicting the effect of an immunosuppressant drug FTY720 on blocking naive CD8+ T cell egress from lymph nodes.

Ethylene is a key platform chemical in global manufacturing, yet its conventional production via steam cracking is highly energy-intensive and a major source of industrial CO2 emissions. This study proposes a sustainable alternative for ethylene synthesis through the electrochemical reduction of captured CO2 via alkaline electrolysis powered by intermittent offshore wind energy. A selective catalytic pathway for the CO2 reduction reaction is identified and modeled in ASPEN PLUS®, with full integration of reaction, separation, and recycle units. To address the variability in renewable energy supply, a time-variable process optimization framework is developed in Pyomo, enabling operational flexibility through integrated process planning and scheduling. Three electricity sourcing scenarios are analyzed, each representing different balances between grid and renewable power. A gate-to-gate life cycle assessment reveals a significant greenhouse gas emission reduction, with the most renewable... [more]

Contains the Aspen Plus flowsheet files and Python source code for the modelling, simulation, and optimization of a process which converts captured CO2 and electricity into ethylene, considering intermittent electricity.

A technical evaluation on the production of sustainable formaldehyde was presented in this report, including process design, advanced simulation, economic analysis, and environmental analysis. Three process configurations to produce formaldehyde were developed: a base-case with no capture of carbon, a post-combustion capture (PCC) process, which utilized 14 wt.% MEA solution-based process, and a direct air capture (DAC) route which used NaOH. Sequestered CO₂ was used as a major feedstock for methanol production via an electrocatalytic reactor (ECR), after which was converted into formaldehyde via a FORMOX process. Large-scale simulations were carried out, demonstrating a yearly methanol production capacity of approximately 62 million kilograms, with a fixed formaldehyde-to-methanol conversion ratio of 1.4 kg per kg of methanol. Economic models were developed using Aspen Process Economic Analyser, indicating that the base-case option (without capture) would involve a capital expenditure... [more]

Aspen Plus simulations for the conversion of CO2 into Formaldehyde and related processes.

GAMS models and supporting spreadsheets for Innovative Strategies in Sustainable Formaldehyde Production in Belgium: Integrating Process Optimisation, Carbon Capture, and a comprehensive Environmental Assessment.

This is the OpenLCA Database for Innovative Strategies in Sustainable Formaldehyde Production in Belgium: Integrating Process Optimisation, Carbon Capture, and a comprehensive Environmental Assessment.

This is the MATLAB source code for the SATvac (Stochastic Agent-based T-cell Vaccination) model, a stochastic agent-based framework for simulating CD8+ T cell dynamics following vaccination. This model captures main immune response phases including activation, expansion, and contraction. It also tracks T cell differentiation into effector and memory cell types and explains the variability observed in immune responses by modeling stochasticity at the single cell level.

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]

The growing size and complexity of energy system optimization models, driven by high-resolution
spatial data, pose significant computational challenges. This study introduces methods to reduce model’s size and improve computational efficiency while preserving solution accuracy. First, a composite-curve-based approach is proposed to aggregate granular data into larger resolutions without averaging out specific properties. Second, a general clustering method groups geographically proximate fields, replacing multiple transportation arcs with a single arc to reduce transportation-related variables. Lastly, a two-step algorithm that decomposes the supply chain design problems into two smaller, more manageable subproblems is introduced. These methods are applied to a case study of switchgrass-to-biofuels network design in eight U.S. Midwest states, demonstrating their effectiveness with realistic and detailed spatial data.

Three different implementations of the flipped class paradigm were used to teach Chemical Engineering students at Imperial College London (ICL) in the 2023-24 academic year: (1) The 3rd year elective course Introduction to Numerical Methods (INM) taught in its entirety in flipped format (the "good"); (2) The 2nd year core course on Process Dynamics and Control (PDC), with the first half of the course on process dynamics taught in traditional lecture format, and the second half on process control taught in flipped format (the "bad"); and (3) a one-week workshop on heat integration, taught as part of a 3rd year core course on Process Design (PD), taught in flipped format (the "surprising"). This paper describes these three implementations in detail and presents and analyzes the responses from student surveys intended to ascertain students' perceptions about the level of their satisfaction with the flipped class approach and the degree to which they achieved mastery of the taught... [more]

Maintaining sufficient amounts of dissolved oxygen throughout a microbial cultivation is a classic control task in bioprocess engineering to avoid negative effects onto cell physiology and productivity. But traditional PID-based algorithms struggle when faced with pulsed substrate additions and the resulting sudden surge of oxygen uptake. In this work a nonlinear MPC is employed and compared to a PID setup for the cultivation of an E. coli strain exposed to intermittent feeding. Both controllers are tuned for a fast pulse response combined with efficient and robust control action. Their performance was tested in-silico with isolated feed pulses, as well as throughout a full cultivation run. Further, the effects of parameter uncertainty were investigated to assess the impact of a model-plant mismatch. The results showed that the predictive nature of the MPC is well suited for maintaining the dissolved oxygen levels above a threshold and outperforms the PID in almost all investigated sim... [more]

Optimization under uncertainty is inherent to many PSE applications ranging from process design to RTO. Reaching process true optima often involves learning from experimentation, but actual experiments involve a cost (economic, resources, time) and a budget limit usually exists. Finding the best trade-off on cumulative process performance and experimental cost over a finite budget is a Partially Observable Markov Decision Process (POMDP), known to be computationally intractable. This paper follows the nonmyopic Bayesian optimization (BO) approximation to POMDPs developed by the machine-learning community, that naturally enables the use of hybrid plant surrogate models formed by fundamental laws and Gaussian processes (GP). Although nonmyopic BO using GPs may look more tractable, evaluating multi-step decision trees to find the best first-stage candidate action to apply is still expensive with evolutionary or NLP optimizers. Hence, we propose modelling the value function of the first-st... [more]

Active learning is widely recognized as an effective teaching approach that can improve classroom outcomes. This is enabled by providing the time for students to apply new knowledge, make mistakes, correct them, and repeat the process until mastery is achieved. One way to implement active learning is through the flipped classroom paradigm. However, to be effective, active learning depends on providing students with a variety of open-ended problems, ranging in difficulty from introductory to advanced levels. This paper presents four food-themed problems for use in numerical methods and process control courses:
1. Formulating Willy Wonka’s new chocolate bar: An introductory linear programming problem focused on translating verbal descriptions into mathematical models.
2. Optimal production for the Matrix Pizza company: A more advanced mixed-integer linear programming problem involving multiple scheduling scenarios.
3. Optimal frying time for fried ice cream production: A transient hea... [more]