Data-Driven Techniques and Data Analytics in Water Treatment Facilities: Innovative Safety Protocols and Optimization
Paul Quarshie *
Kwame Nkrumah University of Science and Technology Kumasi Ghana, Youngstown State University, Ohio, United States.
Temitope Isaiah Asefon
Kwame Nkrumah University of Science and Technology Kumasi Ghana, Youngstown State University, Ohio, United States.
*Author to whom correspondence should be addressed.
Abstract
Aim: To examine harnessing of data-driven techniques and data analytics in water treatment facilities considering innovative safety protocols and process optimization.
Problem Statement: The most significant part of human existence and industrial operations has been linked to water which often becomes vulnerable to hazardous contaminants which are brought on through natural processes and human activity.
Significance of Study: The use of water treatment facilities in order to make water a sustainable natural resource for the whole world is vital. To achieve this, it is imperative to monitor and classify water quality.
Methodology: Recent literature materials in form of books, journals and relevant published articles in the area of data-driven techniques and data analytics in water treatment facilities were consulted.
Discussion: This review article has critically examined harnessing of data-driven and data analytics in water treatment facilities with consideration also given to innovative safety protocols and process optimization. The basic fundamental principle of water treatment facilities were discussed alongside the application of data-driven techniques. Safety protocols in water treatment facilities were discussed. Data driven techniques in water treatment facilities and applications of machine learning and artificial intelligence techniques to water treatment were explained. It was noticed that AI-Driven solutions transforms real-time water quality analysis and offers a proactive approach to water treatment. The integration of machine learning and AI algorithms capabilities in water treatment facilities can now respond quickly to fluctuations and continuously monitor key parameters fluctuations in water quality. Consideration was given to artificial intelligence as data analytics optimization tool for water treatment facilities. Future prospects, limitations and challenges of the study were stated.
Conclusion: AI algorithms and historical data can be harnessed into water treatment facilities in order to assess performance patterns, predict equipment malfunctions and proactively plan the maintenance activities.
Keywords: Data-driven techniques, water treatment facilities, safety protocols, process optimization, data analytics