IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i9p3604-d352022.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/9/3604/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/9/3604/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Majid Dehghani & Hossein Riahi-Madvar & Farhad Hooshyaripor & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Kwok-wing Chau, 2019. "Prediction of Hydropower Generation Using Grey Wolf Optimization Adaptive Neuro-Fuzzy Inference System," Energies, MDPI, vol. 12(2), pages 1-20, January.
    2. 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.
    3. J. M. Pearce, 2016. "Return on investment for open source scientific hardware development," Science and Public Policy, Oxford University Press, vol. 43(2), pages 192-195.
    4. Geraldo Cardoso de Oliveira Neto & Luiz Eduardo Carvalho Chaves & Luiz Fernando Rodrigues Pinto & José Carlos Curvelo Santana & Marlene Paula Castro Amorim & Mário Jorge Ferreira Rodrigues, 2019. "Economic, Environmental and Social Benefits of Adoption of Pyrolysis Process of Tires: A Feasible and Ecofriendly Mode to Reduce the Impacts of Scrap Tires in Brazil," Sustainability, MDPI, vol. 11(7), pages 1-18, April.
    5. Di Sarno, Cesario & Garofalo, Alessia & Matteucci, Ilaria & Vallini, Marco, 2016. "A novel security information and event management system for enhancing cyber security in a hydroelectric dam," International Journal of Critical Infrastructure Protection, Elsevier, vol. 13(C), pages 39-51.
    6. Gökçen Uysal & Aynur Şensoy & A. Arda Şorman & Türker Akgün & Tolga Gezgin, 2016. "Basin/Reservoir System Integration for Real Time Reservoir Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(5), pages 1653-1668, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Walter Cardoso Satyro & Jose Celso Contador & Jose Luiz Contador & Marco Aurélio Fragomeni & Sonia Francisca de Paula Monken & Ana Freitas Ribeiro & Anderson Ferreira de Lima & Jansen Anderson Gomes &, 2021. "Implementing Industry 4.0 through Cleaner Production and Social Stakeholders: Holistic and Sustainable Model," Sustainability, MDPI, vol. 13(22), pages 1-16, November.
    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. Ren, Xiaocong & Huang, Zilong & He, Yiqun, 2024. "Financial warning for coal mining investments: Evidence from the fruit fly optimisation algorithm with backpropagation neural networks," Energy Economics, Elsevier, vol. 134(C).
    5. 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).
    6. 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.
    7. 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.
    8. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jiaxin Yu & Jun Wang, 2020. "Optimization Design of a Rain-Power Utilization System Based on a Siphon and Its Application in a High-Rise Building," Energies, MDPI, vol. 13(18), pages 1-18, September.
    2. González, Santiago G. & Dormido Canto, S. & Sánchez Moreno, José, 2020. "Obtaining high preventive and resilience capacities in critical infrastructure by industrial automation cells," International Journal of Critical Infrastructure Protection, Elsevier, vol. 29(C).
    3. Kaloop, Mosbeh R. & Bardhan, Abidhan & Kardani, Navid & Samui, Pijush & Hu, Jong Wan & Ramzy, Ahmed, 2021. "Novel application of adaptive swarm intelligence techniques coupled with adaptive network-based fuzzy inference system in predicting photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    4. Yi Liu & Zhiqiang Jiang & Zhongkai Feng & Yuyun Chen & Hairong Zhang & Ping Chen, 2019. "Optimization of Energy Storage Operation Chart of Cascade Reservoirs with Multi-Year Regulating Reservoir," Energies, MDPI, vol. 12(20), pages 1-20, October.
    5. Wei, Hu & Hongxuan, Zhang & Yu, Dong & Yiting, Wang & Ling, Dong & Ming, Xiao, 2019. "Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks," Applied Energy, Elsevier, vol. 250(C), pages 389-403.
    6. Prince Waqas Khan & Yung-Cheol Byun & Sang-Joon Lee & Namje Park, 2020. "Machine Learning Based Hybrid System for Imputation and Efficient Energy Demand Forecasting," Energies, MDPI, vol. 13(11), pages 1-23, May.
    7. Irina Glushankova & Aleksandr Ketov & Marina Krasnovskikh & Larisa Rudakova & Iakov Vaisman, 2019. "End of Life Tires as a Possible Source of Toxic Substances Emission in the Process of Combustion," Resources, MDPI, vol. 8(2), pages 1-10, June.
    8. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.
    9. A. Moridi & J. Yazdi, 2017. "Optimal Allocation of Flood Control Capacity for Multi-Reservoir Systems Using Multi-Objective Optimization Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4521-4538, November.
    10. Julio Barzola-Monteses & Mónica Mite-León & Mayken Espinoza-Andaluz & Juan Gómez-Romero & Waldo Fajardo, 2019. "Time Series Analysis for Predicting Hydroelectric Power Production: The Ecuador Case," Sustainability, MDPI, vol. 11(23), pages 1-19, November.
    11. Yongqi Liu & Hui Qin & Li Mo & Yongqiang Wang & Duan Chen & Shusen Pang & Xingli Yin, 2019. "Hierarchical Flood Operation Rules Optimization Using Multi-Objective Cultured Evolutionary Algorithm Based on Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(1), pages 337-354, January.
    12. Shahaboddin Shamshirband & Masoud Hadipoor & Alireza Baghban & Amir Mosavi & Jozsef Bukor & Annamária R. Várkonyi-Kóczy, 2019. "Developing an ANFIS-PSO Model to Predict Mercury Emissions in Combustion Flue Gases," Mathematics, MDPI, vol. 7(10), pages 1-16, October.
    13. Saeed Nosratabadi & Amir Mosavi & Shahaboddin Shamshirband & Edmundas Kazimieras Zavadskas & Andry Rakotonirainy & Kwok Wing Chau, 2019. "Sustainable Business Models: A Review," Sustainability, MDPI, vol. 11(6), pages 1-30, March.
    14. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    15. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    16. Ji-Jian Lian & Xin-Yu Guo & Chao Ma & Kui Xu, 2019. "Optimal Reservoir Flood Control Operation Using a Hedging Model and Considering the Near-Field Vibrations Induced by Flood Release," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2645-2663, June.
    17. Yixuan Guo & Xiao Liang & Ziyu Niu & Zezhou Cao & Liuwei Lei & Hualin Xiong & Diyi Chen, 2021. "Vibration Characteristics of a Hydroelectric Generating System with Different Hydraulic-Mechanical-Electric Parameters in a Sudden Load Increasing Process," Energies, MDPI, vol. 14(21), pages 1-21, November.
    18. Fatemeh Jafari & S. Jamshid Mousavi & Jafar Yazdi & Joong Hoon Kim, 2018. "Real-Time Operation of Pumping Systems for Urban Flood Mitigation: Single-Period vs. Multi-Period Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4643-4660, November.
    19. Md Mijanur Rahman & Mohammad Shakeri & Sieh Kiong Tiong & Fatema Khatun & Nowshad Amin & Jagadeesh Pasupuleti & Mohammad Kamrul Hasan, 2021. "Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks," Sustainability, MDPI, vol. 13(4), pages 1-28, February.
    20. Mosavi, Amir & Faghan, Yaser & Ghamisi, Pedram & Duan, Puhong & Ardabili, Sina Faizollahzadeh & Hassan, Salwana & Band, Shahab S., 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," OSF Preprints jrc58, Center for Open Science.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3604-:d:352022. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.