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]

This work explores the economic and environmental opportunities for sustainable aviation fuel (SAF) in the Brazilian sugarcane industry. Brazil was one of the first countries to use biomass fuels for transportation and is currently the 2nd largest producer of the world’s bioethanol. Bioethanol produced from sugarcane can be upgraded to SAF via the American Society for Testing and Materials (ASTM)-certified pathway alcohol-to-jet (ATJ); however, at least two challenges exist for commercial implementation. First, technologies to produce bio-jet fuels cost more than their conventional fossil-based counterparts. Second, there is considerable uncertainty regarding returns on investment as the sugar and ethanol markets have been historically volatile. As such, we propose a new optimization model to inform risk-conscious investment decisions on SAF production capacity in sugarcane mills. Specifically, we propose a linear program (LP) to model an integrated sugarcane mill that can produce suga... [more]

This paper presents optimal design for an energy-integrated biogas-fuel cell system for renewable electricity generation. The integrated process consists of two steps. The first step generates hydrogen from biogas via methane steam reforming (SMR), whereas the second step electrochemically converts this hydrogen into electricity using a solid oxide fuel cell (SOFC). These two steps are coupled via material and energy integration. Specifically, various design alternatives like anode and/or cathode gas recycling, biogas upgradation by CO2 removal, external versus direct internal reforming, and auxiliary power production through steam and/or micro gas turbine are explored to improve the overall efficiency and total annualized cost of the system. Specifically, a flowsheet superstructure is developed by incorporating all the available design alternatives. An optimal flowsheet with minimum total annualized cost is extracted from this superstructure using formal optimization techniques to mee... [more]

Reverse electrodialysis (RED) is a nascent renewable technology that generates clean, baseload electricity from salinity differences between two water streams, a renewable source known as salinity gradient energy (SGE). Full-scale RED progress calls for robust techno-economic and environmental assessments. Using generalized disjunctive programming (GDP) and life cycle assessment (LCA) principles, this work proposes cost-optimal and sustainable RED process designs involving different RED stack sizes and width-over-length ratios to guide the design and operation from the demonstration to full-scale phases. Results indicate that RED units will benefit from larger aspect ratios with a relative increase in net power of over 30% with 6 m2 membrane size. Commercial RED unit sizes (0.25–3 m2) require larger aspect ratios to reach an equal relative increase in net power but exhibit higher power densities. The GDP model devises profitable RED process designs for all the assessed aspect ratios in... [more]

Most studies in process systems engineering are applying incomplete methods when incorporating sustainability. Including sustainability is a laudable goal, and practitioners are encouraged to develop systems that promote economic, environmental, and social aspects. Ten methods that are often overlooked in performing sustainable process systems engineering are listed in this effort and discussed in detail. Practitioners are encouraged to create designs that are inherently safer, to be more complete in their identification of process chemicals used and released, to be complete in their definitions of supply chains, and to apply additional environmental impact categories. Other methods point to items that are factors in process systems engineering such as disruptive recycling, robust superstructures for optimizations, and employing complete sets of objectives. Finally, users should be aware that sustainability tools are available, which might have been outside of their awareness.

Sand produced along with well-production fluids accumulates in the surface facilities over time, taking valuable space, while the sand carried with the fluids damages downstream equipment. Thus, sand is separated from the fluid in the sand traps and separators and removed during periodic clean-ups. But at high sand productions, the probability of unscheduled facilities shutdowns increases. Such extreme production conditions can be handled by strategic planning and optimal design of the separator network to enable maximum sand separation at minimal equipment cost while ensuring the accumulation extent is within tolerable limits. This paper develops a mathematical model to optimize the separator network design to maximize sand separation while the sand accumulation extent and total equipment cost are minimal. The optimization model is formulated using multi-objective mixed-integer nonlinear programming (MINLP). The capabilities of the developed model to assist sand management in the sepa... [more]

Computer-aided molecular and process design (CAMPD) tries to find the best molecules together with their optimal process. If the optimization problem considers two or more components as degrees of freedom, the resulting mixture design is challenging for optimization. The quality of the solution strongly depends on the accuracy of the thermodynamic model used to predict the thermophysical properties required to determine the objective function and process constraints. Today, most molecular design methods employ thermodynamic models based on group counts, resulting in a loss of structural information of the molecule during the optimization. Here, we unlock CAMPD based on property prediction methods beyond first-order group-contribution methods by using molecule superstructures, a graph-based molecular representation of chemical families that preserves the full adjacency graph. Disjunctive programming is applied to optimize molecules from different chemical families simultaneously. The de... [more]

In this work, we propose a Mixed Integer Nonlinear Programming (MINLP) model to determine the optimal sustainable design of a poly(hydroxyalkanoate)s (PHAs) production plant configuration and its heat exchanger network (HEN). The superstructure-based optimization model considers different carbon sources as raw material: glycerol (crude and purified), corn starch, cassava starch, sugarcane sucrose and sugarcane molasses. The PHA extraction section includes four alternatives: the use of enzymes, solvent, surfactant-NaOCl or surfactant-chelate. Model constraints include detailed capital cost for equipment, mass and energy balances, product specifications and operating bounds on process units. To assess the feasibility of the PHA plant, we considered the Sustainability Net Present Value (SNPV) as the objective function, a multi-criteria sustainability metric that considers economic, environmental and social pillars. The Net Present Value (NPV) was also calculated. SNPV metric provides usef... [more]