Demographic shifts and increased automation in chemical plants are reducing the experience and skill levels of plant operators. Therefore, BASF has implemented Operator Training Simulators (OTS) to allow operators to practice and improve their skills in this safe and controlled environment. The OTS consists of a dynamic model of the process, a control system and safety logics. This paper describes the learnings from using OTS at BASF, where they are used to train operators in process understanding, optimization, procedural training, and disturbance handling. Benefits include reduced training costs, minimized risks and improved efficiency. Also organizational guidelines are provided to ensure that the mentioned benefits are realized in industrial practice. Additionally, high-accuracy OTS models support HAZOP, debottlenecking, and optimization studies.

The increasing global population has resulted in the scramble for more energy. Hydrogen offers a new revolution to energy systems worldwide. Considering its numerous uses, research interest has grown to seek sustainable production methods. However, hydrogen production must satisfy three factors, i.e., energy security, energy equity, and environmental sustainability, referred to as the energy trilemma. Therefore, this study seeks to investigate the sustainability of hydrogen production pathways through the use of a Multi-Criteria Decision- Making model. In particular, a modified Simultaneous Evaluation of Criteria and Alternatives (SECA) model was employed for the prioritization of 19 options for hydrogen production. This model simultaneously determines the overall performance scores of the 19 options and the objective weights for the energy trilemma in a South African context. The results obtained from this study showed that environmental sustainability has a higher objective weight v... [more]

Pressure-swing distillation (PSD) is a frequently applied method to separate pressure-sensitive azeotropic mixtures; however, its energy demand is very high. In continuous mode, PSD is performed in a system consisting of a high- and a low-pressure column. If the composition of the feed is between the azeotropic compositions at the two pressures, it can be introduced into any of the columns, leading to two possible column sequences. Depending on the feed composition, one of the sequences is optimal whether in terms of energy demand or total annual cost (TAC). In the present work, surrogate model-based optimization is applied to determine the optimal TAC value as a function of the feed composition between the azeotropic ones. As a first step, the column sequence with feeding into the high-pressure column is studied here. The mixture to be separated consists of water and ethylenediamine, which form a maximum-boiling azeotrope. The columns are modeled separately and a large number of simul... [more]

This study evaluates the introduction of Carbon Capture and Utilization (CCU) process in two Colombian refineries, focusing on their potential to reduce CO2 emissions and their associated impacts under a scenario aligned with the Net Zero Emissions by 2050 Scenario defined in the 2023 IEA report. The work uses a MILP programming tool (Linny-R) to model the operational processes of refinery sites, incorporating a net total cost calculation to optimize process performance over five-year intervals. This optimization was constrained by the maximum allowable CO2 emissions. The methodology includes the calculation of surplus refinery off-gas availability, the selection of products and CCU technologies, and the systematic collection of data from refinery operations, as well as scientific and industrial publications. The results indicate that integrating surplus refinery fuel gas (originally used for combustion processes) and HTL bio-crude off-gas (as a source of biogenic CO2) can significantl... [more]

Dynamic modelling of wind turbines and their simulation is a very useful tool for studying their behaviour. One of the key elements concerning the physical models of wind turbines is the power coefficient Cp, which acts as an efficiency in the extraction of power from the wind. Unfortunately, this coefficient is often unknown a priori, as it does not usually appear in the information provided by manufacturers. This paper first describes a methodology for obtaining the power coefficient parameters of a commercial wind turbine model using the power curve provided by the manufacturer, which indicates the theoretical power that the wind turbine can produce at each wind speed. To achieve this, a parameter estimation problem is formulated and solved to determine the power coefficient parameters. Nevertheless, this information is often insufficient, requiring additional knowledge, such as operational data, to improve the fit. Finally, a new parameter estimation is performed using only real da... [more]

This study presents the application of a Q-learning algorithm to optimize the selection of chemical reactions for methane reforming processes. Starting with a set of 11 candidate reactions, the algorithm identified three key reactions. These reactions effectively represent the experimental data while aligning with the underlying physics of the process and previously reported findings. The algorithm employed an epsilon-greedy policy to balance exploration and exploitation during the training process. Furthermore, simulations based on the identified reactions revealed trends consistent with experimental data. This work highlights the efficiency and adaptability of Q-learning in modeling complex catalytic systems and provides a framework for further exploration and optimization of methane reforming processes.

Contains 423 original peer-reviewed research articles presented at the 35th European Symposium on Computer Aided Process Engineering (ESCAPE 35). Subject categories include Modelling and Simulation, Sustainable Product Development and Process Design, Large Scale Design and Planning/Scheduling, Model Based Optimisation and Advanced Control, Concepts, Methods and Tools, Digitalization and AI, CAPEing with Societal Challenges, CAPE Education and Knowledge, PSE4Food and Biochemical, and PSE4BioMedical and (Bio)Pharma.

This study evaluates the introduction of Carbon Capture and Utilization (CCU) process in two Colombian refineries, focusing on their potential to reduce CO2 emissions and their associated impacts under a scenario aligned with the Net Zero Emissions by 2050 Scenario defined in the 2023 IEA report. The work uses a MILP programming tool (Linny-R) to model the operational processes of refinery sites, incorporating a net total cost calculation to optimize process performance over five-year intervals. This optimization was constrained by the maximum allowable CO2 emissions. The methodology includes the calculation of surplus refinery off-gas availability, the selection of products and CCU technologies, and the systematic collection of data from refinery operations, as well as scientific and industrial publications. The results indicate that integrating surplus refinery fuel gas (originally used for combustion processes) and HTL bio-crude off-gas (as a source of biogenic CO2) can significantl... [more]

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 proposed model focuses on yields and several properties, such as octane number (ON) pre-dictions, in the gasoline production. External streams such as ethanol and methyl terc-butyl ether (MTBE) are imported to the petroleum refinery complementing the gasoline production when boosting ON quality; these imports are considered exogenous independent variables (IVs). On the other hand, numerous trade-offs exist inside the refinery walls (the endogenous IVs) when producing the so-called pure petroleum-refined gasoline (PPRG). These diverse manufacturing IVs (endogenous factors) interplaying with out-of-refinery walls or exogenous options such as ethanol blending and banning MTBE for sustainable liquid fuels are simulated and optimized in NLP problems, whereby linear approaches are proposed in the tailored modeling and optimiza-tion in the search for optimal solutions.

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.

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]

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 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.

Process control should be one of the most exciting chemical engineering undergraduate courses! This presentation describes our experience transforming "Chemical Process Control" into "Data Analytics, Optimization, and Control" at the University of Notre Dame (required in the second semester of the junior year). In six hands-on experiments, students practice data-centric modeling and analysis using the Ardunio-based Temperature Control Lab (TCLab) hardware. The semester learning goals are:
- Develop mathematical models for dynamical systems from data and first principles using modern statistical methods;
- Predict dynamical system performance using numerical methods;
- Analyze, implement, tune, and debug feedback controllers using the hands-on laboratory;
- Formulate and solve optimization problems for decision-making;
- Demonstrate mastery of at least two of the above skills in an open-ended group project.

The goal of this presentation is to share our strategy to modernize... [more]

Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for the semiconductor industry can be solved by constraint answer set programming (CASP) and its solver called clingcon. A successful solution to this problem is achieved, and we found that for all tested problems, CASP is faster and obtains smaller makespan values for seven of the eight problems tested than the solutions based on mixed integer linear programming and constraint paradigms. The considered scheduling problem includes a robot for lot transfers between baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, constraint processing, and Satisfiability Modulo Theories. For a long time, exact methods su... [more]

Contains 134 original peer-reviewed research articles and 10 extended abstracts submitted to FOCAPD 2024. Subject categories include Invited Plenary and Keynote Submissions, Advances in PSE Design, Design and Emerging Fields, Design and Energy Transitions, Design and Sustainability, and Design Education and Future of Design. The scope is process design as it applies to process systems engineering in chemical engineering, energy systems engineering, and related fields.

Flexibility is a critical feature of any industrial system as it tells us about the range of conditions under which the system can effectively and safely operate. It is becoming increasingly important as we face greater volatilities in market conditions, diverse customer needs, more stringent safety and environmental regulations, the growing use of resources with varying availability such as renewable energy, and an increased likelihood of disruptions caused by, for example, extreme weather... (ABSTRACT ABBREVIATED)

The United States’ Inflation Reduction Act (IRA) of 2022 has established incentives to facilitate the energy transition. While these policies provide economic incen-tives that encourage investment and may reduce financial risk for the private sector on the supply side, transitioning to a lower carbon or net-zero economy by 2050 presents several challenges. These include designing flexible production systems that can interact with inter-mittent renewable energy resources, ensure process safety, redesigning existing energy infrastructure to support new energy carriers like hydrogen or ammonia, and making long-term investment decisions in an uncertain and evolving market... (ABSTRACT ABBREVIATED)

Some educators place an emphasis on the commonalities between engineering mathematics with process control, among others and this helps students see the bigger picture of what is being taught. Traditionally, some of the concepts such as diffusion and heat transfer are taught with a mathematical point of view. Now-a-days, Machine Learning (ML) has emerged as topic of greater interest to both educators and learners and new and disparate modules are sometimes introduced to teach the same. With the emergence of these new topics, some students (falsely) believe that ML is a new field that is somehow different and not linked to engineering mathematics. In this work, we show the link between the different topics from engineering mathematics, that are traditionally taught in UG education, with ML. We hope that educators and learners will appreciate the treatise and think differently, and we further hope that this will further increase the interest to improve ML models.

Equation-Oriented (EO) modeling techniques have been gaining popularity as an alternative for simulating and optimizing process systems due to their flexibility and ability to leverage state-of-the-art solvers inaccessible to many procedural modeling approaches. Despite these advantages, adopting EO modeling tools remains challenging due to the significant learning curve and effort required to build and solve models. Many techniques are available to help diagnose problems with EO process models and reduce the effort required to create and use them. However, these techniques still need to be integrated into EO modeling environments, and many modelers are unaware of sophisticated EO diagnostic tools. To survey the availability of model diagnostic tools and common workflows, the U.S. Department of Energy’s Institute for the Design of Advanced Energy Systems (IDAES) has conducted user experience interviews of users of the IDAES Integrated Platform (IDAES-IP) for process modeling. The inter... [more]

Equation-oriented (EO) modeling has the potential to enable the effective design and optimization of the operation of advanced energy systems. However, advanced modeling of energy systems results in a large number of variables and non-linear equations, and it can be difficult to search through these to identify the culprit(s) responsible for convergence issues. The Institute for the Design of Advanced Energy Systems Integrated Platform (IDAES-IP) contains a tool to identify poorly scaled constraints and variables by searching for rows and columns of the Jacobian matrix with small L2-norms so they can be rescaled. A further singular value decomposition can be performed to identify degenerate sets of equations and remaining scaling issues. This work presents an EO model of a flowsheet developed for post-combustion carbon capture using a monoethanolamine (MEA) solvent system as a case study. The IDAES diagnostics tools were successfully applied to this flowsheet to identify problems to im... [more]