Canadian Journal of Chemical Engineering最新文献

In order to handle the overwhelming effects of the removed hydrogen sulphide (H₂S) from natural gas and industrial waste gases on the environment, H₂S can be converted to elemental sulphur. Among the available processes for sulphur recovery, the most widely employed process is a modified Claus process. In this work, first, least square version of support vector machine (LS-SVM) approach is utilized for determining the properties of sulphur including heat of vaporization, heat of condensation (S₆, S₈), heat of dissociation (S₆, S₈), and heat capacity of equilibrium sulphur vapours as a function of temperature. An illustrative example is given to show the usefulness of the presented computer-based models with two parameters for designing and operation of the Claus sulphur recovery unit (SRU). According to the error analysis results, predicted values by the proposed intelligent models are in excellent agreement with the reported data in the literature for the aforementioned sulphur properties where the coefficient of determination (R²) is higher than 0.99 for all developed models. The average absolute relative deviation percent (%AARD) is less than 1.3 while predicting the heat capacity of equilibrium sulphur vapours. Other proposed models' predictions show less than 0.2% AARD from the target values. In addition, a mathematical algorithm on the basis of the Leverage approach is proposed to define the domain of applicability of the developed LS-SVM models. It was found that the presented models are statistically valid and the employed data points for developing the models are within the range of their applicability.

We review the utility and application of artificial intelligence (AI) and machine learning (ML) at various process scales in this work, from molecules and reactions to materials to processes, plants, and supply chains; furthermore, we highlight whether the application is at the design or operational stage of the process. In particular, we focus on the distinct representational frameworks employed at the various scales and the physics (equivariance, additivity, injectivity, connectivity, hierarchy, and heterogeneity) they capture. We also review AI techniques and frameworks important in process systems, including hybrid AI modelling, human-AI collaborations, and generative AI techniques. In hybrid AI models, we emphasize the importance of hyperparameter tuning, especially in the case of physics-informed regularization. We highlight the importance of studying human-AI interactions, especially in the context of automation, and distinguish the features of human-complements-AI systems from those of AI-complements-human systems. Of particular importance in the AI-complements-human framework are model explanations, including rule-based explanation, explanation-by-example, explanation-by-simplification, visualization, and feature relevance. Generative AI methods are becoming increasingly relevant in process systems engineering, especially in contexts that do not belong to ‘big data’, primarily due to the lack of high quality labelled data. We highlight the use of generative AI methods including generative adversarial networks, graph neural networks, and large language models/transformers along with non-traditional process data (images, audio, and text).

Surrogate-based optimization has gained significant traction in several engineering fields, given its ability to handle complex systems without significant computational burdens. However, surrogate models are approximations and may have limitations, including the possibility of artificial minima. The main contribution of this work is the derivation of a robustness test that guarantees the optimality of surrogate-based optimization. The derivation of this metric is based on the universal approximation theorem. The full framework proposed in this work is also composed by a sampling sizing methodology to randomly select samples within a feasible operating region (FOR) resulting from the optimization population, reducing the computational cost of the analysis and avoiding biases in the robustness calculation. The applicability and importance of this methodology are demonstrated through a case study of a complex chemical process—a pressure swing adsorption (PSA) unit—which presents a high computational cost to solve optimization problems. The results highlight the need and importance of evaluating the optimality of surrogate-based optimization schemes.