Venue & metadata

• Journal/Proceedings: Proceedings - European Council for Modelling and Simulation, ECMS
• Volume: 2023-June
• Pages: 575 – 581
• Note: Cited by: 5
• Author keywords: Chemical engineering; Machine Learning; Marine pollution; Scrubber; Sulphur dioxide absorption

Abstract

Modern environmental regulations require rigorous optimization of operations in process engineering to reduce waste, pollution, and risks while maximizing efficiency. However, the nature of chemical plants, which include components with non-linear behavior, challenges the use of consolidated tuning and control techniques. Instead, ad-hoc, self-adapting, and time-variant controls, with a balanced tuning of parameters at both the subsystem and system level, may be necessary. Needed computing processes may require significant resources and high performance systems, if managed by means of traditional approaches and with exact solution methods. In this regard, domain experts suggest instead the use of integrated techniques based on Artificial Intelligence (AI), which include Explainable AI (XAI) and Trustworthy AI (TAI), which are unique in this industry and still in the early stages of development. To pave the way for a real-time, cost-effective solution for this problem, this paper proposes an AI-based approach to model the performance of a real chemical plant, i.e. a marine scrubber installed on a Ro-Ro ship. The study aims to investigate Machine Learning (ML) techniques which can be used to model such processes. Notably, this analysis is the first of its kind, at the best of the authors’ knowledge. Overall, the study highlights the potential of using ML-based techniques, to optimize environmental compliance in the shipping industry. © ECMS Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni (Editors) 2023.

Keywords

Chemical plants GS Machine learning GS Marine pollution GS Pollution control GS Ships GS Sulfur dioxide GS Control techniques GS In-process GS Machine learning approaches GS Machine-learning GS Nonlinear behaviours GS Operating performance GS Optimisations GS Self adapting GS Sulfur dioxide absorption GS Waste pollution GS Cost effectiveness GS

Links & artifacts

Publisher Copy BibTeX Download .bib

{"key"=>"Campanile2023575", "type"=>"Conference paper", "bibtex"=>"@conference{Campanile2023575,\n  author = {Campanile, Lelio and Di Bonito, Luigi Piero and Iacono, Mauro and Di Natale, Francesco},\n  title = {Prediction of chemical plants operating performances: a machine learning approach},\n  year = {2023},\n  journal = {Proceedings - European Council for Modelling and Simulation, ECMS},\n  volume = {2023-June},\n  pages = {575 – 581},\n  type = {Conference paper}\n}\n", "author"=>"Campanile, Lelio and Di Bonito, Luigi Piero and Iacono, Mauro and Di Natale, Francesco", "author_array"=>[{"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Luigi Piero", "last"=>"Di Bonito", "prefix"=>"", "suffix"=>""}, {"first"=>"Mauro", "last"=>"Iacono", "prefix"=>"", "suffix"=>""}, {"first"=>"Francesco", "last"=>"Di Natale", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Lelio", "author_0_last"=>"Campanile", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Luigi Piero", "author_1_last"=>"Di Bonito", "author_1_prefix"=>"", "author_1_suffix"=>"", "author_2_first"=>"Mauro", "author_2_last"=>"Iacono", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Francesco", "author_3_last"=>"Di Natale", "author_3_prefix"=>"", "author_3_suffix"=>"", "title"=>"Prediction of chemical plants operating performances: a machine learning approach", "year"=>"2023", "journal"=>"Proceedings - European Council for Modelling and Simulation, ECMS", "volume"=>"2023-June", "pages"=>"575 – 581", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163436467&partnerID=40&md5=2e96d04affd9bb4a126b224d7cc8d75a", "abstract"=>"Modern environmental regulations require rigorous optimization of operations in process engineering to reduce waste, pollution, and risks while maximizing efficiency. However, the nature of chemical plants, which include components with non-linear behavior, challenges the use of consolidated tuning and control techniques. Instead, ad-hoc, self-adapting, and time-variant controls, with a balanced tuning of parameters at both the subsystem and system level, may be necessary. Needed computing processes may require significant resources and high performance systems, if managed by means of traditional approaches and with exact solution methods. In this regard, domain experts suggest instead the use of integrated techniques based on Artificial Intelligence (AI), which include Explainable AI (XAI) and Trustworthy AI (TAI), which are unique in this industry and still in the early stages of development. To pave the way for a real-time, cost-effective solution for this problem, this paper proposes an AI-based approach to model the performance of a real chemical plant, i.e. a marine scrubber installed on a Ro-Ro ship. The study aims to investigate Machine Learning (ML) techniques which can be used to model such processes. Notably, this analysis is the first of its kind, at the best of the authors’ knowledge. Overall, the study highlights the potential of using ML-based techniques, to optimize environmental compliance in the shipping industry. © ECMS Enrico Vicario, Romeo Bandinelli, Virginia Fani, Michele Mastroianni (Editors) 2023.", "author_keywords"=>"Chemical engineering; Machine Learning; Marine pollution; Scrubber; Sulphur dioxide absorption", "keywords"=>"Chemical plants; Machine learning; Marine pollution; Pollution control; Ships; Sulfur dioxide; Control techniques; In-process; Machine learning approaches; Machine-learning; Nonlinear behaviours; Operating performance; Optimisations; Self adapting; Sulfur dioxide absorption; Waste pollution; Cost effectiveness", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 5"}
    

Suggested citation

Campanile, L., Di Bonito, L. P., Iacono, M., & Di Natale, F. (2023). Prediction of chemical plants operating performances: a machine learning approach [Conference paper]. Proceedings - European Council for Modelling and Simulation, ECMS, 2023-June, 575–581. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85163436467&partnerID=40&md5=2e96d04affd9bb4a126b224d7cc8d75a