Special Issue "Big Data and Artificial Intelligence in Sustainable Water and Wastewater Management"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: 31 October 2021.

Special Issue Editor

Prof. Dr. Jae Kwang (Jim) Park
E-Mail Website
Guest Editor
Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, WI 53706, United States
Interests: biological wastewater treatment; biological nutrient removal; drinking water treatment plant design and optimization; the fate of contaminants in the environment; hazardous and industrial waste treatment; waste reuse and recycling; river restoration; water quality modeling

Special Issue Information

Dear Colleagues,

We have been scratching our head on big data that are collected in water and wastewater management with the question, “now what?” Artificial intelligence (AI) provides new opportunities to use big data for better insight into optimization, control, and sustainability. Furthermore, deterministic models are limited in real-time prediction due to the inability to formulate complex processes. There have been substantial research activities in water and wastewater industries to apply AI for big data analysis. This special issue is aimed to compile research activities that occurred all over the world on water and wastewater management and to provide future research directions.

The scope of the special issue is as follows:

  1. Water quality management in rivers, lakes, and ocean
  2. Decision making for infrastructure investment
  3. Water treatment plant operation and optimization
  4. Wastewater treatment plant operation and optimization
  5. Energy management in water and wastewater management

Prof. Dr. Jae Kwang (Jim) Park
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • big data
  • infrastructure
  • machine learning
  • sustainability
  • wastewater treatment
  • water quality
  • water treatment

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Article
A Prescriptive Intelligent System for an Industrial Wastewater Treatment Process: Analyzing pH as a First Approach
Sustainability 2021, 13(8), 4311; https://doi.org/10.3390/su13084311 - 13 Apr 2021
Viewed by 570
Abstract
An important issue today for industries is optimizing their processes. Therefore, it is necessary to make the right decisions to carry out these activities, such as increasing the profit of businesses, improving the commercial strategies, and analyzing the industrial processes performance to produce [...] Read more.
An important issue today for industries is optimizing their processes. Therefore, it is necessary to make the right decisions to carry out these activities, such as increasing the profit of businesses, improving the commercial strategies, and analyzing the industrial processes performance to produce better goods and services. This work proposes an intelligent system approach to prescribe actions and reduce the chemical oxygen demand (COD) in an equalizer tank of a wastewater treatment plant (WWTP) using machine learning models and genetic algorithms. There are three main objectives of this data-driven decision-making proposal. The first is to characterize and adapt a proper prediction model for the decision-making scheme. The second is to develop a prescriptive intelligent system based on expert’s rules and the selected prediction model’s outcomes. The last is to evaluate the system performance. As a novelty, this research proposes the use of long short-term memory (LSTM) artificial neural networks (ANN) with genetic algorithms (GA) for optimization in the WWTP area. Full article
Show Figures

Figure 1

Back to TopTop