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 sup-ply 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.

This document contains supplementary materials for full-paper submission to ESCAPE 35 - European Symposium on Computer Aided Process Engineering.

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.

Supplementary Material for the contribution "Synthesis of Distillation Flowsheets with Reinforcement Learning using Transformer Blocks" by Niklas Slager and Meik Franke (UTwente) for ESCAPE 35

The complete stochastic-based optimization model for the CSP-TCES-OFC and CSP-TCES-ASU processes are given here.

The transition to renewable energy sources, such as biogas, requires purification processes to separate methane from carbon dioxide, with adsorption-based methods being widely employed. Accurate simulations of these systems, governed by coupled PDEs, ODEs, and algebraic equations, critically depend on precise parameter determination. While traditional approaches often result in significant errors or complex procedures, optimization algorithms provide a more efficient and reliable means of parameter estimation, simplifying the process, improving simulation accuracy, and enhancing the understanding of these systems.
This work introduces an Artificial Intelligence-based methodology for estimating the isotherm parameters of a mathematical phenomenological model for fixed-bed experiments. The separation of CO₂ and CH₄ is used as case study. This work develops an algorithm for parameter estimation for the system's mathematical model. The results show that the validated model has a close fi... [more]

L-lactic acid (L-LA), a key monomer in biodegradable plastics, is a sustainable alternative that can be derived from LCB. The L-LA production process typically involves several various technologies such as fermentation, filtration, and distillation. In the L-LA production process, large amounts of buffers are used to maintain proper pH during fermentation, so conventional buffers (e.g., CaCO3) are often selected because of their low cost. However, these buffers cannot be recycled efficient-ly, and the potential for alternative buffers remains uncertain. In this work, we aim to develop and evaluate novel processes for sustainable L-LA production using the alternative buffer (i.e., KOH). The processes involve a series of different unit operations such as pretreatment, fermentation, ex-traction, and electrolysis. An efficient buffer regeneration process using membrane electrolysis is implemented to recycle the buffer with minimal energy input. Then, we evaluated the viability of the propo... [more]

Deploying engineered carbon dioxide removal strategies like direct air capture (DAC) will be
imperative to meet net-zero targets, given the biophysical constraints associated with land-based
methods. Thoroughly devised deployment pathways for DAC could ease the financial burdens of
adopting the technology at climate-relevant scales. Key drivers for the optimal regional rollouts of
DAC include the available energy supply and geological capacities for CO2 storage. These factors
also impose temporal constraints considering the evolving nature of energy systems and the
development of CO2 transport and storage infrastructure over time. As DAC technologies exhibit
differences in their system demands, integrating various methods into deployment strategies may
address potential trade-offs arising from regional resource endowments. Commercial-ready options
use solid sorbents or liquid solvents, while emerging methods extend to electrochemical devices
like bipolar membranes. These tech... [more]

The document is the digital supplementary material for the article titled "Application of Artificial Intelligence in process simulation tool", submitted to the ESCAPE 35 conference. It contains additional information and figures.

This model uses the CHEMCAD unit operation Batch Column together with tools for data reconciliation and optimization. Some experimental data is included.
It is used as a template in the course Green Processes at Berlin University of Applied Science (BHT), where students use it to regress measured data from lab experiments and to design an optimal process.

This model uses standard CHEMCAD unit operations and thermodynamic models to simulate the separation of ethanol and water from a fermenter broth.
It is used as a template in the course Green Processes at Berlin University of Applied Science (BHT), where students use it to regress measured data from lab experiments and to design an optimal process.

This model uses the kinetic model from Foglers textbook "Elements of Chemical Reaction Engineering" to describe the fermentation of glucose to ethanol.
It is used as a template in the course Green Processes at Berlin University of Applied Science (BHT), where students use it to regress measured data from lab experiments and to design an optimal process.

This document contains digital supplementary material (detailed model results and complete database) related to the article "A two-level model to assess the economic feasibility of renewable urea production from agricultural wastes" which is submitted to the peer reviewed conference proceedings of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35).

This document contains supplementary material related to the article "Valorization of refinery fuel gas and biogenic gases from thermochemical conversion into low-carbon methanol", submitted to the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35).

The reduction and recovery of organic fraction of municipal solid waste is a major challenge for contemporary society. It requires the establishment of regional strategies with minimized environ-mental impact. This study employs life cycle assessment to evaluate the respective environmental performances of the current French system based on incineration, and those of alternative systems including (i) anaerobic digestion with composting and (ii) composting for biowaste treatment under different energy scenarios. The environmental impacts of Parisian waste are calculated by consid-ering the French energy mix in 2022, the average European energy mix in 2022 and the projected French energy mix for 2030. The results show that the proportion of fossil-based sources in the energy mixes significantly influences the environmental performance of waste management sys-tems. Systems with high fossil-based sources dependency tend to favour incineration-based pro-cessing systems. This is driven by th... [more]

The PDF document includes five figures that complement the article "Surface Tension Data Analysis for Advancing Chemical Engineering Applications." The figures illustrate the data distribution for parameters A and B, as well as the quality of the regressions for both parameters.

This document contains digital supplementary material (detailed model description, parameters for different case studies and figure of exemplary waste heat supply and heat demand) related to the article "A Data-Driven Conceptual Approach to Heat Pump Sizing in Chemical Processes with Fluctuating Heat Supply and Demand" which is submitted to the peer reviewed conference proceeding of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35).

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.

The document is the digital supplementary material for the article titled "Synthesis of Liquid Mixture Separation Networks Using Multi-Material Membranes", submitted to the ESCAPE 35 conference. It contains the mathematical formulation, data tables, and additional figures.

This document contains digital supplementary material (model validation, flowsheets and detailed simulation/optimisation results) related to the article entitled “Modelling of a Heat Recovery System (HRS) Integrated with Steam Turbine Combined Heat and Power (CHP) Unit in a Petrochemical Plant”, which is part of the peer reviewed conference proceeding of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35).

This document is supplementary information for the article titled Materials-Related Challenges of Energy Transition, submitted to the ESCAPE 35 conference. It contains the data used in the calculations and referenced in the main article.

The submission contains problems names with the initial guesses that were used to solve them for the reference of the reader.

This code is of a Non-Convex Mixed Integer Quadratic Programme of a hydrogen export facility. This programme is a multistage stochastic model, using inheritance based indexing in order to account for the influence of uncertainty in weather forecasting on the optimal scheduling of the export facility.

In this file can be found:
- Energy demands and scope 1 CO2 emissions for different lime production routes
- Assumptions underlying the price scenarios
- Commodities prices used for cost estimation

To match the growing demand for biomethane production, anaerobic digestors need an optimal and time-varying adaptation of the input diet. Dynamic co-digestion constitutes a hard challenge for the limited instrumentation and control equipment typically installed aboard full-scale plants. The development of prediction models is foreseen to support process (optimal) design and control. In this work, a rigorous framework is applied to take full-scale applicability into account while dealing with the design and training of both high-fidelity and control-oriented first-principle/grey-box models, intended to be exploited for real-time optimization and actual process control respectively.