Model simulation flowsheet of biomass gasification combined cycle system for simultaneous power and hydrogen production coupled with direct air capture in Aspen Plus.

Aspen Plus is used to simulate the process and compute the mass and energy flows for the complete process of polystyrene waste conversion into value-added chemicals. This demonstrates a pathway for the recycling of PS waste, which could contribute to a more sustainable industry environment

We designed a novel methylcyclohexane–toluene–hydrogen (MTH) based chemical heat pump (CHP) and determined its key performance indicators using Aspen Plus process simulations.

This document compiles the digital supplementary material associated with the article entitled “Optimizing Steam Flux for Energy Efficiency in Ammonia Recovery during Sodium Carbonate Production”, published in the peer-reviewed proceedings of the 36th European Symposium on Computer Aided Process Engineering (ESCAPE 36). It presents the effects of variations in steam pressure and temperature on the system’s temperature and mass flow rate.

CCS is a well investigated and fairly promising technology for reducing the emission of carbon dioxide (CO2) to the atmosphere. However, it is rarely implemented in the industry due to its high cost. Therefore, this work proposes a cost optimized CCS chain which can be operated flexibly and safely. For the capture process a post combustion chemical absorption technology is chosen due to its retrofitting possibility to already existing power plants and its low capture cost. In order to find a cost efficient absorption process for different scenarios, the five most promising process configurations from previous work are combined into a superstructure in a rigorous rate based reactive Aspen Plus model. This in turn is optimized by a two-stage stochastic programming approach in Matlab. The optimal supply chain network is identified by a tailor made transshipment model implemented in GAMS, which accounts for the most promising transportation units, storage sites as well as direct utilizatio... [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.

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

While process simulations often are either very rigid and accurate or very flexible and unprecise, informed decision making can only be maintained by establishing a detailed process model as early as possible within the project lifecycle while keeping relevant aspects of the process flexible enough. In this work, we present the development of a framework based on a dynamic interface between AspenPlus® process simulations and Python, enabling enhanced flexibility and automation for process modeling and optimization. This integration leverages the powerful simulation capabilities of AspenPlus® with the versatility of Py-thon for data analysis and optimization, delivering significant improvements in workflow efficiency and process control. By utilizing the dynamic simulation data exchange with Python, extensive parameter studies can be conducted.
In this provided dataset, the necessary input data, as well as the output files for each parameter run are provided. Furthermore, a .runtime an... [more]

This study presents a simple tool to provide decision-makers data that will facilitate informed decisions in selecting utilization for small- to medium-scale utilization of stranded natural gas resources that would otherwise be flared set to be flared. The methodology involves the simulation of different natural gas utilization technologies on Aspen Plus simulation software and utilizing the results to develop a tool on Python that enables the user to assess recoverable valuable products from different natural gas profiles. Ten utilization technologies were implemented, and six different natural gas profile (rich and lean) were used as case studies to ascertain the capabilities of the tool. The supplimentary material provides the interface of the proposed tool.

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]

Water lily (Eichhornia crassipes) has been identified as an invasive exotic plant with high proliferation in Mexico, affecting aquatic bodies, such as lakes. After extraction, the water hyacinth biomass can be used as raw material for the production of bioproducts and bioenergy, however, the majority of them not covered the region's needs, and their economic profitability decreases significantly. Also, few reports present its use as raw material inside a biorefinery scheme. In this work, we propose a local biorefinery scheme to produce power and bioproducts from water lilies, using Aspen Plus V.10.0, per the needs of the Patzcuaro Lake community in Michoacán, Mexico. The scheme has been designed to process the harvested and sun-dried water lily from 197.6 kg/h of total wet harvested biomass, according to the extraction region schedule. The biomass is separated: root (RT) and stems-leaves (SL). The processing scheme involves the RT combustion to produce electric power, and two process... [more]

UCL Chemical Engineering ensures graduates are digitally literate by integrating computational tools like gPROMS, Aspen Plus, and GAMS into the undergraduate curriculum. Students in the first year of undergraduate program use GAMS to solve simple simulation and optimization problems and gPROMS for solving ordinary differential equations (ODEs) in reactor design problems. In the second year, students start using Aspen Plus to simulate more complex chemical process units, interpret and discuss results obtained and justify any differences observed between experimental data and computational results. They use GAMS to simulate and optimize a process flowsheet with considerations of the implications of proper initialization procedures and strategies for obtaining optimal parameters and gPROMS for advanced reactor and separator problems. The computational knowledge acquired in the first two years prepares students for the third-year capstone design project where they use the various tools in... [more]

A transition to low-carbon fuels is integral in addressing the challenge of climate change. An essential transformation is underway in the transportation sector, one of the primary sources of global greenhouse gas emissions. The electrofuels that represent methanol synthesis via power-to-fuel technology have the potential to decarbonize the sector. This paper outlines a critical comprehensive life cycle assessment for electrofuels, with this study focusing on the production of synthetic methanol from renewable hydrogen from water electrolysis coupled with carbon from the direct air capture (DAC) process. This study has provided a comparison of the environmental impacts of synthetic methanol produced from grids of five regions (India, the US, China, Switzerland, and the EU) with conventional methanol from coal gasification and natural gas reforming. The results from this impact assessment show a high dependency of environmental scores on the footprint of the grid. Switzerland, with its... [more]

The separation of 1,3-butadiene from C4 hydrocarbon mixtures is a crucial step in the production of synthetic rubbers and plastics. Conventional extractive distillation methods using solvents, like N,N-dimethylformamide (DMF), have proven effective but presents significant health and environmental challenges. This study explores the feasibility of using propylene carbonate (PC) as a green solvent alternative for butadiene extractive distillation, leveraging its environmentally friendly properties and industrial compatibility. Simulations were conducted using Aspen Plus®, employing the Non-Random Two-Liquid (NRTL) model coupled with the Redlich-Kwong equation of state to describe phase equilibrium. Results indicate that PC integrates seamlessly into existing processes, achieving comparable operational stability and butadiene separation efficiency with minimal modifications. A significant design improvement was the elimination of the methylacetylene separation column in the PC process, w... [more]

Detailed assessment of fuel production processes at an early stage of a project is crucial to identify potential technical challenges, optimize efficiency and minimize costs and environmental impact. While process simulations often are either very rigid and accurate or very flexible and unprecise, informed decision making can only be maintained by establishing a detailed process model as early as possible within the project lifecycle while keeping relevant aspects of the process flexible enough. In this work, we present the development of a framework based on a dynamic interface between AspenPlus® process simulations and Python, enabling enhanced flexibility and automation for process modeling and optimization. This integration leverages the powerful simulation capabilities of AspenPlus® with the versatility of Python for data analysis and optimization, delivering significant improvements in workflow efficiency and process control. By utilizing the dynamic simulation data exchange with... [more]

This study presents a simple tool to provide decision-makers data that will facilitate informed decisions in selecting utilization for small- to medium-scale utilization of stranded natural gas resources that would otherwise be flared. The methodology involves the simulation of different natural gas utilization technologies on Aspen Plus simulation software and utilizing the results to develop a tool on python that enables the user to assess recoverable valuable products from different natural gas profiles. Ten utilization technologies were implemented and six different natural gas profiles (rich and lean) were used as case studies to ascertain the capabilities of the tool. The results provide the user with the Net Present Values (NPV) of different technologies and the most profitable or infeasible utilization technology. The results also show the potentials of utilizing the gas over flaring. For very small volumes of gas the results favored the compressed natural gas (CNG) with positi... [more]

New research on complex intensified distillation schemes has popularized the use of several commercial process simulation software. The interfaces between process simulation and optimization-oriented software have allowed the use of rigorous and robust models. This type of optimization is mentioned in the literature as "Black Box Optimization", since successive evaluations exploits the information from the simulator without altering the model that represents the given process. Among process simulation software, Aspen Plus® has become popular due to their rigorous calculations, model customization, and results reliability. This work proposes a comparative study for Aspen Plus software and Microsoft Excel VBA®, Python® and MATLAB® interfaces. Five distillation schemes were analyzed: conventional column, reactive column, extractive column, column with side rectifier and a Petlyuk column. The optimization of the ?????? (Total Annual Cost) was carried out by a modified Simulated Annealing A... [more]

Hydrogen isotope separation is a critical component of the fusion fuel cycle, particularly for achieving the desired purity levels of deuterium and tritium while minimising tritium inventory. This study investigates the cryogenic distillation of hydrogen isotopes, with a focus on the effects of isotopic equilibrium reactions at reduced temperatures and different system configurations. A one-column architecture was analysed to evaluate the impact of feed and side stream equilibrator temperatures and flowrates on separation performance and tritium inventory. Additionally, a two-column architecture was studied, incorporating multiple isotopic equilibrators in interconnecting streams, to further reduce unwanted heteronuclear isotopologues and improve system efficiency. Comparative analysis of the proposed configurations highlights significant operational advantages of optimising equilibrator temperatures, including reduced tritium contamination and inventory. Results indicate that reducing... [more]

Recently, the focus on increasing process efficiency to reduce energy consumption has driven the adoption of alternative systems, such as Petlyuk distillation columns. It has been proven that, when compared to conventional distillation columns, these systems offer significant energy and cost savings. From an economic standpoint, achieving high-purity products alone does not ensure the feasibility of a process. Instead, balancing the trade-off between product purity and cost necessitates multi-objective optimization. While conventional optimization methods are effective, novel strategies like Bayesian optimization offer distinct advantages for handling complex systems. Bayesian optimization requires no explicit mathematical model and can efficiently optimize even when starting from a single initial point. However, as a black-box method, it demands a detailed analysis of hyperparameters, such as the acquisition function and the number of initial points, to ensure optimal performance. Thi... [more]

The deployment of CO2 capture technologies at a large scale will largely benefit from the knowledge acquired during pilot testing. A mobile CO2 capture pilot unit is currently being designed at the University of Liège. Here, the pilot plant is introduced, and the column sizing results are presented. The sizing was performed with a process model built in Aspen Plus. Overall, the pilot installation is expected to serve for process model validation, data collection and technology de-risking while assisting Belgian industries in their transition towards carbon neutrality.

This paper evaluates the thermodynamic efficiency of a newly synthesized large-scale pre-combustion CO2 capture process using a novel ionic liquid (IL) 1-octyl-2,3-methylimidazolium thiocyanate [OMMIM][SCN] for blue H2 production. In addition, the potential eco-toxicity of the selected IL was assessed using the ADMETlab 2.0 web tool. The results of these analyses were compared to those of an established IL 1-butyl-2,3-dimethylimidazolium bis(trifluoromethyl sulfonyl)imide [BMMIM][TF2N]. The eco-toxicity assessment confirmed that [OMMIM][SCN] is less environmentally toxic than [BMMIM][TF2N]. Thermodynamic analysis of the novel system shows the COOLER unit accounts for the highest energy demand; however, the [OMMIM][SCN] system demonstrates a 7.45% reduction in energy consumption in the COOLER unit compared to [BMMIM][TF2N]. The system experienced the highest exergy losses (irreversibilities) in the COOLER unit for [BMMIM][TF2N] (12982 kW) and in the flash separator unit for [OMMIM][SCN]... [more]

The co-gasification of biomass and plastic waste offers a promising pathway for sustainable syngas production, necessitating precise prediction of its composition to optimize efficiency. This study compares the performance of Aspen Plus models, including the thermodynamic equilibrium model (TEM) and restricted thermodynamic equilibrium model (RTM), with machine learning (ML) techniques, focusing on the support vector regression (SVR) for syngas prediction during steam and air co-gasification. Aspen Plus simulations provided valuable mechanistic insights, while the ML model demonstrated superior predictive accuracy. The SVR, enhanced by principal component analysis (PCA), significantly improved performance, achieved R² values of 0.879 for H2, 0.856 for CO, 0.859 for CO2, and 0.744 for CH4 on the testing dataset. It also outperformed other models in terms of RMSE, achieving exceptional precision for CH4 (0.0087), CO (0.0193), and H2 (0.0194). In contrast, RTM exhibited moderate accuracy... [more]

The urgent need for sustainable energy drives the exploration of biomass and plastic waste co-gasification, a promising route for producing clean fuels and chemicals, reducing greenhouse gas emissions, and minimizing fossil fuel dependence. Modeling and simulation are vital for optimizing this process, particularly syngas yield, yet comparative studies on Aspen Plus modeling techniques for steam co-gasification are limited. This research addresses this gap by comparing three Aspen Plus strategies: thermodynamic equilibrium modeling (TEM), restricted thermodynamic modeling (RTM), and kinetic modeling (KM), for simulating the co-gasification of pine sawdust and polyethene (PE) with steam in bubbling fluidized bed gasifier (BFBG). The primary objective is to evaluate the effectiveness of each strategy in predicting the syngas composition under varying conditions. Three models were developed in Aspen Plus on the basis of each strategy, and their predicted syngas compositions were compared... [more]

This is an Aspen Plus V14 model of a furnace process. Natural gas is combusted in excess air to produce heat. A heat exchanger model simulates the boiling of boiler feed water near 29 bar pressure to produce steam at about 235 deg C. Design specs are used to ensure certain process conditions and scales are met. This is supplementary material for the paper Adams TA. Exergy in Chemical Engineering Education, submitted to the ESCAPE 35 conference.

Contains optimized design data, aspen simulation files for the three chemical heat pumps namely:
Isopropanol–acetone–hydrogen
2-Butanol–methyl ethyl ketone–hydrogen
2-Pentanol–methyl propyl ketone–hydrogen.
Optimization code (written in python) is also provided.