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Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles

Author

Listed:
  • Marcos Geraldo Gomes

    (Industrial Engineering Post-Graduation Program, Universidade Nove de Julho, Sao Paulo 01504-001, Brazil)

  • Victor Hugo Carlquist da Silva

    (Industrial Engineering Post-Graduation Program, Universidade Nove de Julho, Sao Paulo 01504-001, Brazil)

  • Luiz Fernando Rodrigues Pinto

    (Industrial Engineering Post-Graduation Program, Universidade Nove de Julho, Sao Paulo 01504-001, Brazil)

  • Plinio Centoamore

    (Industrial Engineering Post-Graduation Program, Universidade Nove de Julho, Sao Paulo 01504-001, Brazil)

  • Salvatore Digiesi

    (Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, 70125 Bari, Italy)

  • Francesco Facchini

    (Department of Mechanics, Mathematics and Management, Polytechnic University of Bari, 70125 Bari, Italy)

  • Geraldo Cardoso de Oliveira Neto

    (Industrial Engineering Post-Graduation Program, Universidade Nove de Julho, Sao Paulo 01504-001, Brazil)

Abstract

Due to the increasing demand for water supply of urban areas, treatment and supply plants are becoming important to ensure availability and quality of this essential resource for human health. Enabling technologies of Industry 4.0 have the potential to improve performances of treatment plants. In this paper, after reviewing contributions in scientific literature on I4.0 technologies in dam operations, a study carried out on a Brazilian dam is presented and discussed. The main purpose of the study is to evaluate the economic, environmental, and social advantages achieved through the adoption of Artificial Intelligence (AI) in dam operations. Unlike automation that just respond to commands, AI uses a large amount of data training to make computers able to take the best decision. The current study involved a company that managed six reservoirs for treatment systems supplying water to almost ten million people at the metropolitan area of São Paulo City. Results of the study show that AI adoption could lead to economic gain in figures around US$ 51,000.00 per year, as well as less trips between sites and less overtime extra costs on the main operations. Increasing gates maneuvers agility result in significant environmental gains with savings of about 4.32 billion L of water per year, enough to supply 73,000 people. Also, decreasing operational vehicle utilization results in less emissions. Finally, the AI implementation improved the safety of dam operations, resulting in social benefits such as the flood risk mitigation in cities and the health and safety of operators.

Suggested Citation

  • Marcos Geraldo Gomes & Victor Hugo Carlquist da Silva & Luiz Fernando Rodrigues Pinto & Plinio Centoamore & Salvatore Digiesi & Francesco Facchini & Geraldo Cardoso de Oliveira Neto, 2020. "Economic, Environmental and Social Gains of the Implementation of Artificial Intelligence at Dam Operations toward Industry 4.0 Principles," Sustainability, MDPI, vol. 12(9), pages 1-19, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3604-:d:352022
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    References listed on IDEAS

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    1. Benyou Jia & Slobodan P. Simonovic & Pingan Zhong & Zhongbo Yu, 2016. "A Multi-Objective Best Compromise Decision Model for Real-Time Flood Mitigation Operations of Multi-Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3363-3387, August.
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    Cited by:

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    2. Ke-Liang Wang & Rui-Rui Zhu & Yun-He Cheng, 2022. "Does the Development of Digital Finance Contribute to Haze Pollution Control? Evidence from China," Energies, MDPI, vol. 15(7), pages 1-21, April.
    3. Samia Bouazza & Zoubida Benmamoun & Hanaa Hachimi, 2023. "Maritime Bilateral Connectivity Analysis for Sustainable Maritime Growth: Case of Morocco," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    4. Kumar, Anil & Agrawal, Rohit & Wankhede, Vishal A & Sharma, Manu & Mulat-weldemeskel, Eyob, 2022. "A framework for assessing social acceptability of industry 4.0 technologies for the development of digital manufacturing," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Geraldo Cardoso de Oliveira Neto & Auro de Jesus Cardoso Correia & Henrricco Nieves Pujol Tucci & Rosângela Andrade Pita Brancalhão Melatto & Marlene Amorim, 2023. "Reverse Chain for Electronic Waste to Promote Circular Economy in Brazil: A Survey on Electronics Manufacturers and Importers," Sustainability, MDPI, vol. 15(5), pages 1-27, February.
    6. Walter Cardoso Satyro & Jose Celso Contador & Sonia Francisca de Paula Monken & Anderson Ferreira de Lima & Gilberto Gomes Soares Junior & Jansen Anderson Gomes & João Victor Silva Neves & José Robert, 2023. "Industry 4.0 Implementation Projects: The Cleaner Production Strategy—A Literature Review," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    7. André Felipe Henriques Librantz & Fábio Cosme Rodrigues dos Santos, 2023. "Intelligent Clustering Techniques for the Reduction of Chemicals in Water Treatment Plants," Sustainability, MDPI, vol. 15(8), pages 1-18, April.

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