In Quebec, the chemical sector is rapidly changing, with old facilities closing and new ones opening. Similar situation is happening in other geographies as well. Utilities need to understand the energy needs of these industries, particularly as they transition towards decarbonization. By studying existing data, they can estimate energy requirements and identify alternative technologies such as heat pumps, electric boilers, biomass boilers, and green hydrogen. Two key indicators to measure decarbonization performance: the Decarbonization Efficiency Coefficient and the GHG Performance Indicator. Decarbonizing could significantly reduce energy use, depending on the selected technologies, leading to variations of 6.1 TWh for electricity and 3.5 TWh for biomass.

Predicting the critical micelle concentration (CMC) of surfactants is essential for optimizing their applications in various industries, including pharmaceuticals, detergents, and emulsions. In this study, we investigate the per-formance of graph-based machine learning models, specifically Graph Convolutional Networks (GCN), Graph At-tention Networks (GAT), and a graph-transformer model, PharmHGT, for predicting CMC values. We aim to de-termine the most effective model for capturing the structural and physicochemical properties of surfactants. Our results provide insights into the relative strengths of each approach, highlighting the potential advantages of transformer-based architectures like PharmHGT in handling molecular graph representations compared to traditional graph neural networks. This comparative study serves as a step towards enhancing the accuracy of CMC predictions, contributing to the efficient design of surfactants for targeted applications.

Soybean is a highly valuable global commodity due to its versatility and numerous derivative products. During harvest, all non-seed materials become “straw”. Currently, this waste is pri-marily used for low-value purposes such as animal feed, landfilling, and incineration. To address this, the present work proposes a conceptual biorefinery aimed at converting soybean straw into higher-value products. The study began with data collection to identify potential conversion routes. Based on this information, a superstructure was developed, comprising seven conversion routes: four thermochemical routes (pyrolysis, combustion, hydrothermal gasification, and lique-faction), two biological routes (fermentation and anaerobic fermentation), and one chemical route (alkaline extraction). Each process was evaluated based on product yields, conversion times, and associated capital and operating costs. Using this data, an MILP (Mixed-Integer Linear Program-ming) optimization model was built in Pyomo u... [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 applica-tions remain largely overlooked in the literature. While CC cost is often used as a key perfor-mance indicator (KPI) for CC technologies, the lack of standardized cost estimation methods leads to inconsistencies, complicating comparisons, and hindering the deployment of CC sys-tems. 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 liter... [more]

This document contains digital supplementary material (LCA model including parametrized LCI with sources, unit processes and LCA results) related to the article "Integrated LCA and Eco-design Process for Hydrogen Technologies: Case Study of the Solid Oxide Electrolyser" which is submitted to the peer reviewed conference proceeding of the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35).

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).

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.

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.

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.

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

The submission includes SimaPro project for LCA of SOFCs with 10-year replacement plan, a text file explaining how to run the SimaPro file, and SOFC inventory tables provided in an Excel file.