Artificial Photosynthetic Bio-Electrochemical Systems (AP-BES) offer a promising approach for converting CO2 to valuable bioproducts, addressing carbon mitigation and sustainable production. This study employs a stochastic techno-economic assessment (TEA) to estimate the viability of rhodopsin driven AP-BES, from carbon capture to product purification. Unlike traditional deterministic TEAs, this approach uses Monte Carlo simulations to model uncertainties in key technoeconomic parameters, including energy consumption, CO2 conversion efficiency, and bioproduct market prices. The analysis generates probability distributions for economic metrics such as Operational Expenditure (OPEX), Capital Expenditure (CAPEX), and profit. Enhancements in light-harvesting efficiency and advancements in reactor materials were predicted to reduce the payback period to just one year, thereby making large-scale deployment a feasible option.

Liquid-liquid extraction stages often deviate from equilibrium due to factors like insufficient mixing, making accurate efficiency modeling essential for process simulation. This study addresses the limitations of Aspen Plus (AP), which distorts equilibrium calculations by directly multiplying efficiency with the distribution coefficient. A modified Murphree efficiency definition, more suitable for liquid-liquid systems but absent in AP's Extraction Column module, was implemented using Aspen Custom Modeler (ACM). The custom multi-stage extraction column model replaces mole fractions with mole flows to better represent mass transfer and phase interactions, enhancing simulation accuracy when imported into AP. Two test cases validated the custom model's effectiveness. Test Case I, utilizing the UNIQ-RK thermodynamic model, compared the ACM model to AP's built-in module, revealing that the ACM model provides a more realistic representation of extraction processes under varying stage effici... [more]

Surrogate models are widely used to improve the tractability of process optimization. Some commonly used surrogate models are obtained via machine learning such as multi-layer perceptrons (MLPs), Gaussian processes, and decision trees. Recently, a new class of machine learning models named Kolmogorov Arnold Networks (KANs) have been proposed. Broadly, KANs are similar to MLPs, yet they are based on the Kolmogorov representation theorem instead of the universal approximation theorem for the MLPs. Compared to MLPs, it was reported that KANs require significantly fewer parameters to approximate a given input/output relationship. One of the bottlenecks preventing the embedding of MLPs into optimization formulations is that MLPs with a high number of parameters (larger width or depth) are more challenging to globally optimize. We investigate whether the parameter efficiency of KANs relative to MLPs can be translated to computational benefits when embedding them into optimization problems an... [more]

Developing piping and instrumentation diagrams (P&IDs) is a fundamental task in process engineering. For designing complex installations, such as petroleum plants, multiple departments across several companies are involved in refining and updating these diagrams, creating significant challenges in data exchange between different software platforms from various vendors. The primary challenge in this context is interoperability, which refers to the seamless exchange and interpretation of information to collectively pursue shared objectives. To enhance the P&ID creation process, a unified, machine-readable data format for P&ID data is essential. A promising candidate is the Data Exchange in the Process Industry (DEXPI) standard. We present pyDEXPI, an open-source implementation of the DEXPI format for P&IDs in Python. pyDEXPI makes P&ID data more efficient to handle, more flexible, and more interoperable. We envision that, with further development, pyDEXPI will act as a central scientific... [more]

The green transition accelerates innovations and developments targeting the integration of green hydrogen in the chemical industry. However, all new hydrogen pathways and process designs must be tested on operability and safety. A big challenge is the typical fluctuating characteristic of green hydrogen supply that contrasts the steady-state operation of most conventional chemical processes. Therefore, to adequately assess control and monitoring techniques, a benchmark model tailored to the relevant aspects of the hydrogen economy is required. We introduce a benchmark model based on the production of green ammonia using the Haber-Bosch process that remains operable when coupled to a fluctuating hydrogen supply from water electrolysis. The main section of the process model is an adiabatic indirect cooled reactor system that provides realistic modeling of industrial applications. Like the ammonia reactor, all process units and the underlying control structure are precisely dimensioned to... [more]

Rapid and robust simulation of chemical production processes is critical to address core scientific questions related to process design, optimization, and sustainability. Efficiently solving a chemical process, however, remains a challenge due to their highly coupled and nonlinear nature. Graph abstractions of the underlying physical phenomena within unit operations may help identify potential avenues to systematically reformulate the network of equations and enable more robust convergence of flowsheets. To this end, we further refined a flowsheet graph-theoretic abstraction that consists of a mesh of interconnected variable nodes and equation nodes. The new network of equations is formulated at the phenomenological level agnostic to the thermodynamic property package by extending equation formulations widely used to solve multistage equilibrium columns. Decomposition of the graph by phenomena linearizes material and energy balances across the flowsheet by decoupling phenomenological n... [more]

A key step in developing high-performance industrial products lies in the design of their constituent molecules. Computer-aided molecular design (CAMD) has garnered significant attention for its potential to accelerate and improve the design process. The mainstream method involves using property prediction models to predict the properties of potential molecules and selecting the best candidates based on these predictions. However, prediction errors are inevitable, introducing unreliability into the design. To address this issue, this paper proposes a novel component property modeling framework based on a molecular similarity coefficient. By calculating the similarity between a target molecule and those in an existing database, the framework selects the most similar molecules to form a tailored training dataset. The similarity coefficient also quantifies the reliability of the property predictions. In tests across various properties, this framework not only provides a quantifiable evalu... [more]

Process systems engineering optimization problems may be challenging. These problems often exhibit nonlinearity, non-convexity, discontinuity, and uncertainty, and often only the values of objective and constraint functions are accessible. Additionally, some problems may be computationally expensive. In such scenarios, black-box optimization methods may be appropriate to tackle such problems. A general-purpose multi-agent framework for optimization has been developed to automate the configuration and use of hybrid optimization, allowing for multiple optimization solvers, including different instances of the same solver. Solvers can share solutions, leading to better outcomes with the same computational effort. Alongside cooperation, competition is introduced by dynamically allocating more computational resource to solvers best suited to the problem. Each solver is assigned a priority that adapts to the evolution of the search. The scheduler is priority-based and uses similar algorithms... [more]

Uncertainty plays a crucial role in strategic supply chain design. In this study, we explore robust approaches to model uncertainty when the non-deterministic parameters are placed in the independent term, on the right-hand side (RHS) of the constraints. We consider the "disjunctive adjustable column-wise robust optimization" (DACWRO), a disjunctive formulation introduced previously in our group, and compare it with the adjustable column-wise robust optimization (ACWRO) formulation, a specific technique for solving robust optimization problems when the original robust optimization approach may assume too-conservative results. Given that the proposed method is based on the generalized disjunctive programming (GDP) technique, it is a higher lever modelling approach that represents the discrete nature of the decision process. In addition, it provides alternative MILP representations that can be further tested and compared. The analysis assesses the computational performance and reformulat... [more]

This study presents an integrated approach combining the Analytic Hierarchy Process (AHP) with a Mixed-Integer Multi-Objective Linear Programming (MOMILP) model to evaluate sustainable power generation alternatives for Pamplona, Colombia. The MOMILP model includes solar, wind, biomass, and diesel technologies, aiming to minimize costs (net present value) and CO2 emissions while considering design, operational, and budget constraints. The AHP method evaluates multiple criteria such as social acceptance, job creation, technological maturity, and environmental impact. The results show that solar panels are prioritized, with small diesel plants added due to resource limitations. The most sustainable option is a hybrid system with 49% solar, 29% wind, 14% biomass and 8% diesel, generating a net present value of 121,360 USD and 94,720 kg of CO2 emissions. The proposed methodology can be applied to assess and select the most feasible alternative within a wide range of new projects for the int... [more]

Bayesian optimization is known to be able to search for the optimal conditions based on a small number of experiments. However, these experiments are insufficient to understand the experimental condition space. In contrast, we report the development of an algorithm that combines a low-confounding definitive screening design with Bayesian optimization, allowing for rapid optimization and ensuring sufficient experiments to understand the experimental condition space with a low confounding.

Electricity and carbon trading towards carbon reduction are highly coupled. The research on joint trading is essential for helping companies identify optimal strategies and enabling policymakers to detect potential policy loopholes. This study presents a novel game theory optimization model involving both power generation companies (GenCos) and factories to explore optimal operation strategies under electricity-carbon joint trading. By fully capturing the operational characteristics of power generation units and the technical energy consumption of electricity-consuming enterprises, it describes the relationship between renewable energy, fossil fuels, electricity, and carbon emissions detailedly. Considering the correlation between production volume and price of the same product, the case actually encompasses three trading systems: electricity, carbon, and commodities. Transforming this nonlinear model into a mixed-integer linear form through piecewise linearization and discretization,... [more]

Mechanical compression heat pumps are indispensable to facilitate the transition from thermally driven processes to renewable energy by electrification, upgrading low-temperature waste heat to recycle it at a higher temperature level. However, the implementation of such heat pumps up to date encounters limitations, due to equipment limitations and a lack of tools for the design of process concepts for the application of high-temperature heat pumps. The optimal design of heat pumps relies heavily on the selection of an appropriate refrigerant, as the thermodynamic properties significantly affect the heat pump cycle design and performance. While existing methods are capable of identifying thermodynamically beneficial refrigerants, they do not directly account for practical constraints such as limitations on the compressor discharge temperature, compression ratio, and vacuum operation. The current study proposes a fast-screening approach for arbitrary heat pump applications, considering a... [more]

In process systems engineering (PSE), obtaining accurate process models for optimization can be expensive and time-consuming. Black-box Bayesian Optimization (BO) with Gaussian process (GP) surrogates offers a promising approach. However, full black-box optimization neglects valuable prior knowledge, which could otherwise improve the optimization process. This work explores methods of integrating prior knowledge in the form of low-fidelity data into BO by evaluating these methods on synthetic multi-fidelity test functions. Our results highlight possibilities for improved convergence of the BO optimization. However, our work further highlights potential pitfalls of these multi-fidelity models, such as bias, convergence to local optima, and overfitting on low-fidelity data. Hence, leveraging low-fidelity data in multi-fidelity models can improve BO convergence, but there are instances where the algorithms are more susceptible to failure.

This work approaches a detailed characterization of the aspects inherent to the innovative paradigm of Water and Energy Integration Systems (WEIS). These consists in conceptual physical systems which consider all potential energy-using and water-using processes in a site, all potential recirculation of material and energy streams between these and the integration of several categories of state-of-the-art technologies. The WEIS have the ultimate aim to promote the sustainability character associated to existing installations (through the reduction of energy and water input and contaminants output). The specific characteristics of WEIS are compared to existing similar process integration methodologies and a set of performance indicators are determined, having as a basis two previous case-studies approached for the Engineering project of WEIS. The performed analysis in this work revealed that the innovative paradigm is able to constitute Engineering projects with associated sustainability... [more]

Addressing challenges in process design and optimisation, especially with complex models and data uncertainties, requires effective tools for model development, selection, and identification. Techniques such as Model-based Design of Experiments (MBDoE) help support this task by screening and discriminating between models and, eventually, calibrating them. Open-source and user-friendly Python packages have implemented some model identification techniques. However, the need for a tool that can couple with various model simulators and account for the steps of model identification as well as physical constraints of systems in design of experiments remains unmet. In that light, we present the python package MIDDOE (Model-(based) Identification, Discrimination, and Design of Experiments) to address this gap. It integrates rival models screening, parameter estimation, uncertainty analysis, and MBDoE techniques, while adapting to various process constraints. These functionalities are demonstra... [more]

This paper introduces the Updated Absolute Expected Value Solution, U-AEEV, a heuristic for solving multi-stage stochastic programming (MSSP) problems with type 2 endogenous uncertainty. U-AEEV is an evolution of the Absolute Expected Value Solution, AEEV [1]. This paper aims to show how U-AEEV overcomes the drawbacks of AEEV and performs better than AEEV. To demonstrate the performance of U-AEEV, we solve 6 MSSP problems with type 2 endogenous uncertainty and compare the solutions and computational resource requirements.

This article aims to tutorial a few important extremum seeking control approaches that can be used for the model-free optimization of industrial processes in various fields. The application of several methods is illustrated with a simple case study related to the production of algal biomass in photobioreactors. Other methods and applications are briefly reviewed.

Monitoring chemical composition is key in several industrial-scale chemical processes. However, traditional composition sensors usually convey drawbacks, including high costs, short lifetimes, and frequent calibration requirements. As an alternative, software (soft) sensors have gained attention in recent years due to their accuracy, ease of training, and potential of integrating widely known machine learning techniques. This study presents the methodology followed to train a soft sensor for hydrogen sulfide monitoring in the gas treatment section of an industrial facility in Italy. In particular, this methodology includes a novel approach for steady-state determination from historical plant data in the presence of several steady states and noise. Unfortunately, only four steady states were found in the plant data, which was insufficient for accurate soft sensor training. As an alternative, these steady states were used to develop and validate a rigorous Aspen HYSYS process simulation.... [more]

The application of Real-time Optimization (RTO) to dynamic operations is challenging due to the complexity of the nonlinear problems involved, making it difficult to achieve robust solutions. The literature on RTO in Pressure Swing Adsorption (PSA) units relies on Model Predictive Control (MPC) and Economic Model Predictive Control (EMPC), which rely heavily on an accurate model representation of the industrial plant. Given the importance of PSA systems on multiple separation operations, establishing alternatives for control and optimization in real-time is in order. With that in mind, this work aimed to explore alternative model-free RTO techniques that depend on simple control elements, as is the case of Extremum Seeking Control (ESC).The chosen case study was Syngas Upgrading. Extremum Seeking Control successfully optimized the CO2 productivity in PSA units for syngas upgrading/H2 purification. The results demonstrate that ESC can be a valuable tool in optimizing and controlling PSA... [more]

Implementing Model Predictive Control (MPC) for large-scale nonlinear systems is often computationally challenging due to the intensive online optimization required. To address this, various reduced-order linearization techniques have been developed. The Koopman operator linearizes a nonlinear system by mapping it into an infinite-dimensional space of observables, enabling the application of linear control strategies. While Artificial Neural Networks (ANNs) can approximate the Koopman operator in a data-driven manner, training these networks becomes computationally intensive for high-dimensional systems as the lifting into a higher-dimensional observable space significantly increases data size and complexity. In this work, we propose a technique, combining Proper Orthogonal Decomposition (POD) with an efficient ANN structure to reduce the training time of ANN for large order systems. By first applying POD, we obtain a low order projection of the system. Subsequently, we train the ANN w... [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]

The definition of strategies for operation of process networks is a key research focus in process systems engineering. This challenge is commonly formulated as a numerical constraint satisfaction problem, where most practical algorithms are limited to identifying inner approximations to the feasible operational envelope. Sampling-based approaches so far have only been developed for formulations that required coordinated operation of the units within the network. We propose a decomposition approach that enables decentralized operation for acyclic muti-unit processes by sampling. Our methodology leverages problem structure to decompose unit-wise and deploys surrogate models to couple the resultant subproblems. We demonstrate it on a serial, batch chemical reactor network. In future research, we will extend this framework to consider the presence of uncertain unit parameters robustly.

Reaction processes require optimization to enhance key performance indicators (KPIs) such as yield, conversion, and selectivity. Techniques like Bayesian Optimization (BO), Model-Based Design of Experiments (MBDoE), and Goal-Oriented Optimal Experimental Design (GOOED) play pivotal roles in achieving these objectives. BO efficiently explores the design space to identify optimal conditions, while MBDoE maximizes the information gain by reducing kinetic model uncertainty. In contrast, GOOED focuses solely on maximizing the KPIs without considering the system uncertainty, identifying reactor conditions in the design space guaranteeing optimal performance. This study compares BO, MBDoE, and GOOED in optimizing methane oxidation in an automated flow reactor. Performance is assessed based on optimal methane conversion, reduced system uncertainty and minimal experimental efforts to achieve maximum conversion. BO quickly identifies high-conversion conditions, MBDoE minimizes experimental runs... [more]

This study presents a deterministic optimization methodology for the design of shell-and-tube heat exchangers with twisted tape turbulence promoters, focusing on minimizing the total annualized cost (TAC) while balancing thermal performance and energy consumption. A sensitivity analysis was carried out as Case I (Methanol-Water), it reveals that increasing the twist ratio (TR) reduces flow turbulence, resulting in lower fluid velocity, pressure drop (?Pi), and overall heat transfer coefficient (U). Among the turbulence promoters evaluated, twisted tapes with V-cuts achieved a 21.1% increase in U with a 52.27% increase in pressure drop, demonstrating an optimal balance between thermal enhancement and energy cost. In contrast, promoters with circular rings and multiple perforations showed the highest U improvements (26.7% and 25.8%, respectively) but incurred significant pressure drops (93.5% and 97.9%). The optimization problem has been stated as a nonlinear programming (NLP) problem an... [more]