Digital Chemical Engineering最新文献

This paper presents a novel approach to address computational challenges in predicting flow features by employing a Graph Neural Network (GNN), which is proficient in predicting flow domain values. Traditional Computational Fluid Dynamics (CFD) simulations, although effective, often require substantial computational resources and time, limiting their applicability in time-sensitive scenarios and optimization studies necessitating extensive case studies. The main objective was to evaluate the feasibility of employing node classification on a graph generated from a 2D baffle flow system to segment the domain based on relative fluid age. A second objective was to compare the computational time required for CFD simulations with the inference time of the network to quantify the efficiency gains achieved by utilizing the network. Results demonstrate the potential of utilizing graph convolutional networks for domain segmentation to predict regions of holdup and bypass. The GNN achieved 97% and 92% accuracy in predicting recirculation regions in single and double baffle cases, particularly excelling in high Reynolds number scenarios. Importantly, the proposed GNN-based approach reduces computation time by over 2100%, showcasing significant efficiency gains. Results here highlight the promise of employing graph convolutional networks for flow feature prediction, offering substantial computational efficiency improvements over traditional CFD simulations.

The surge in demand for hydrogen (H₂) across diverse sectors, including clean energy transportation and chemical synthesis, underscores the need for a thorough investigation into H₂ production dynamics and the development of effective controllers for industrial applications. This paper focuses on an electrically heated steam methane reforming (SMR) process for H₂ production, offering advantages such as enhanced environmental sustainability, compactness, efficiency, and controllability compared to conventional reforming methods. Electric heating of the entire system allows for adjustments in current to control reactor temperature, thereby impacting hydrogen production rates. However, accurately modeling hydrogen production dynamics presents a formidable challenge, as complex models with high precision are computationally unsuitable for real-time control integration. Considering these factors, an accurate and efficient first-principles-based lumped-parameter model is developed to provide a dependable estimation of hydrogen production in an electrically-heated steam methane reformer. This model is validated experimentally and then utilized in a model predictive controller (MPC). To obtain the necessary state estimate information for the MPC, an extended Luenberger observer (ELO) method is employed to estimate state variables from limited, infrequent and delayed measurements of gas-phase reactor outlet stream and frequent measurements of the reactor temperature. Simulation comparisons with a proportional-integral (PI) controller reveal a much faster response in achieving the desired H₂ production rate under the estimation-based MPC. Additionally, the simulations demonstrate the robustness of the controller to process variability such as a decrease in catalyst activation energy, commonly encountered in the SMR process, highlighting its effectiveness in maintaining stable operation under varying process conditions.

Machine learning methodologies are gaining significant recognition as an effective approach for tackling and modelling challenges related to membranes. This study delves into the utilization of machine learning algorithms to forecast the quality of reverse osmosis (RO) water. Specifically, we conduct a comparative analysis of four popular algorithms: decision tree, random forest, support vector machine (SVM), and K-nearest neighbours (KNN). Our dataset comprises essential water quality evaluation features such as temperature, pH, and conductivity. Using these features, we train and test our models, evaluating their performance with metrics like accuracy and root-mean-squared error (RMSE). The outcomes indicate that all four algorithms perform admirably in predicting RO water quality, achieving accuracy scores ranging from 80 % to 95 %. Notably, KNN stands out with the highest accuracy score of 95 %, establishing it as the most effective algorithm for this task. Besides its performance, KNN's simplicity of implementation and interpretability make it a pragmatic choice for real-world applications. This study serves as compelling evidence of the potential of machine learning algorithms for forecasting RO water quality, particularly highlighting KNN's effectiveness in this context. To further enhance the accuracy of RO water quality prediction, future research could explore the inclusion of other features or alternative algorithms.

This perspective article highlights our opinions on the imperative of incorporating Process Intensification (PI) into undergraduate chemical engineering education, recognizing its pivotal role in preparing future engineers for contemporary industrial challenges. The trajectory of PI, from historical milestones to its significance in advancing the United Nations’ Sustainable Development Goals (SDGs), reflects its intrinsic alignment with sustainability, resource efficiency, and environmental stewardship. Despite its critical relevance, the absence of dedicated PI courses in numerous undergraduate chemical engineering programs presents an opportunity for educational enhancement. An exploration of global PI-related courses reveals the potential of educational platforms to fill this void. To address this gap, we advocate for the introduction of a standalone PI course as a minor elective, minimizing disruptions to established curricula while acknowledging the scarcity of PI expertise. The challenges associated with PI integration encompass faculty workload, specialized expertise, curriculum content standardization, and industry alignment. Surmounting these challenges necessitates collaborative efforts among academia, industry stakeholders, and policymakers, emphasizing the manifold benefits of PI, faculty development initiatives, and the establishment of continuous improvement mechanisms. The incorporation of PI into curricula signifies a transformative approach, cultivating a cadre of innovative engineers poised to meet the demands of the evolving industrial landscape.

In the Industry 4.0 revolution, industries are advancing their operations by leveraging Artificial Intelligence (AI). AI-based systems enhance industries by automating repetitive tasks and improving overall efficiency. However, from a safety perspective, operating a system using AI without human interaction raises concerns regarding its reliability. Recent developments have made it imperative to establish a collaborative system between humans and AI, known as Intelligent Augmentation (IA). Industry 5.0 focuses on developing IA-based systems that facilitate collaboration between humans and AI. However, potential conflicts between humans and AI in controlling process plant operations pose a significant challenge in IA systems. Human-AI conflict in IA-based system operation can arise due to differences in observation, interpretation, and control action. Observation conflict may arise when humans and AI disagree with the observed data or information. Interpretation conflicts may occur due to differences in decision-making based on observed data, influenced by the learning ability of human intelligence (HI) and AI. Control action conflicts may arise when AI-driven control action differs from the human operator action. Conflicts between humans and AI may introduce additional risks to the IA-based system operation. Therefore, it is crucial to understand the concept of human-AI conflict and perform a detailed risk analysis before implementing a collaborative system. This paper aims to investigate the following: 1. Human and AI operations in process systems and the possible conflicts during the collaboration. 2. Formulate the concept of observation, interpretation, and action conflict in an IA-based system. 3. Provide a case study to identify the potential risk of human-AI conflict.

Imaging and image-based process analytical technologies (PAT) have revolutionized the design, development, and operation of crystallization processes, providing greater process understanding through the characterization of particle size, shape and crystallization mechanisms in real-time. The performance of corresponding PAT models, including machine learning/artificial intelligence (ML/AI)-based approaches, is highly reliant on the data quality used for training or validation. However, acquiring high quality data is often time consuming and a major roadblock in developing image analysis models for crystallization processes.

To address the lack of diverse, high-quality, and publicly available particle image datasets, this paper presents an initiative to create an open-access crystallization-related image database: OpenCrystalData (OCD, at www.kaggle.com/opencrystaldata/datasets). The datasets consist of images from different crystallization systems with different particle sizes and shapes captured under various conditions. The initial release consists of four different datasets, addressing the estimation of particle size distribution using in-situ images for different categories of particles and detection of anomalous particles for process monitoring purposes. Images are collected using various instruments, followed by case-specific processing steps, such as ground-truth labeling and particle size characterization using offline microscopy. Datasets are released on the online collaborative platform Kaggle, along with specific guidelines for each dataset. These datasets are aimed to serve as a resource for researchers to enable learning, experimentation, development, and evaluation and comparison of different analytical approaches and algorithms. Another goal of this initiative is to encourage researchers to contribute new datasets focusing on various systems and problem statements. Ultimately, OpenCrystalData is intended to facilitate and inspire new developments in imaging-based PAT for crystallization processes, encouraging a shift from time-consuming offline analysis towards comprehensive real-time process insights that drive product quality.

Polymer grade scheduling, maritime surveillance, e-food delivery, e-commerce, and military tactics necessitate multiple agents (e.g., extruders, speed boats, salesmen) capable of visiting (or completing) dynamically changing locations (or tasks) in minimum time and distance. This study proposes a novel methodology based on clustering and local heuristic-based evolutionary algorithms to address the dynamic traveling salesman problem (TSP) and the dynamic multi-salesman problem with multiple objectives. The proposed algorithm is evaluated on 11 benchmark TSP problems and large-scale problems with up to 10,000 instances. The results show the superior performance of the proposed methodology called the dynamic two-stage evolutionary algorithm as compared to the dynamic hybrid local search evolutionary algorithm. Furthermore, the algorithm's applicability is illustrated through various scenarios involving up to four salesmen and three objectives with dynamically changing locations. To demonstrate real-world relevance, a maritime surveillance problem employing a helideck monitoring system is solved, wherein the objective is to minimize the patrolling route while visiting faulty vessels that threaten marine vessels. This study provides a general framework of TSP which finds application in several sectors, including planning and scheduling in chemical and manufacturing industries, the defense sector, and the e-commerce sector. Finally, the results showcase the effectiveness of the proposed methodology in solving the dynamic multiobjective, and multiple salesmen problem, which represents a more generalized version of the TSP.

The use of immersive technologies and digital twins can enhance training and learning outcomes in various domains. These technologies can reduce the cost and risk of training and improve the retention and transfer of knowledge by providing feedback in real-time. In this paper, a novel virtual reality (VR) based Bioreactor simulation is developed that covers the set-up and operation of the process. It allows the trainee operator to experience infrequent events, and reports on the effectiveness of their response. An embedded complex simulation of the bioreaction effectively replicates the impact of operator decisions to mimic the real-world experience. The need to train and assess the skills acquired aligns with the requirements of manufacturing in a validated environment, where proof of operator capability is a prerequisite. It has been deployed at UK’s National Horizons Center(NHC) to train the trainees in biosciences.

An issue that can limit the long-term value of information published in peer-reviewed engineering publications is the inability of readers to readily access data contained within a publication. This paper discusses experiences in changing the expectations for data sharing by authors in a large, disciplinary engineering journal, the AIChE Journal, in ways that seek to balance the burdens on authors and the benefits to readers.