Abstract
The objective of this research is to improve the hydrogen production and total profit of a real Steam Reforming plant. Given the impossibility of tuning the real factory to optimize its operation, we propose modelling the plant using Artificial Neural Networks (ANNs). Particularly, we combine a set of independent ANNs into a single model. Each ANN uses different sets of inputs depending on the physical processes simulated. The model is then optimized as a black-box system using metaheuristics (Genetic and Memetic Algorithms). We demonstrate that the proposed ANN model presents a high correlation between the real output and the predicted one. Additionally, the performance of the proposed optimization techniques has been validated by the engineers of the plant, who reported a significant increase in the benefit that was obtained after optimization. Furthermore, this approach has been favorably compared with the results that were provided by a general black-box solver. All methods were tested over real data that were provided by the factory.
Highlights
The chemical industry domain reveals the appearance of many research problems that can be handled with Artificial Intelligence (AI) techniques
We study the performance of using two well-known metaheuristics, Genetic Algorithm (GA) and Memetic Algorithm (MA), in order to determine the best input configuration for the Artificial Neural Networks (ANNs) model, when optimizing the two real and separated optimization problems that were previously introduced: the maximization of the H2 produced by the Steam Reforming (SR) plant, and the maximization of the profit obtained by the sale of the production and the use of other gases in the plant
To evaluate the proposals of this paper, we carry on several experiments in order to confirm the contribution of the model proposed, as well as the metaheuristics used to optimize the inputs to the model
Summary
The chemical industry domain reveals the appearance of many research problems that can be handled with Artificial Intelligence (AI) techniques. The topic of this paper consists of improving the hydrogen production and total profit of a real Steam Reforming (SR) plant using AI techniques. These techniques make possible modelling the behavior of a SR plant avoiding a lengthy and very expensive process of real parameters tuning (sometimes even impossible to perform in the real factory). A better performance of the factory implies a reduction in the products burned in the processes that occurs in the SR plant when producing hydrogen, which has a deep environmental interest. We study a Steam Reforming plant owned by a well-known Spanish petrochemical company, whose industrial processes optimization are the subject of this research paper. The gases produced, i.e., the outputs of the plant, such as gaseous
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