Application of artificial intelligence tools in wastewater and waste gas treatment systems: Recent advances and prospects

被引：10

作者：

Behera, Shishir Kumar ^{[1
]}

Karthika, S. ^{[2
]}

Mahanty, Biswanath ^{[3
]}

Meher, Saroj K. ^{[4
]}

Zafar, Mohd. ^{[5
]}

Baskaran, Divya ^{[6
,7
]}

Rajamanickam, Ravi ^{[8
]}

Das, Raja ^{[9
]}

Pakshirajan, Kannan ^{[10
]}

Bilyaminu, Abubakar M. ^{[11
]}

Rene, Eldon R. ^{[11
]}

机构：

[1] Vellore Inst Technol, Sch Chem Engn, Proc Simulat Res Grp, Vellore 632014, Tamil Nadu, India

[2] Anna Univ, Alagappa Coll Technol, Dept Chem Engn, Chennai 600025, Tamil Nadu, India

[3] Karunya Inst Technol & Sci, Div Biotechnol, Coimbatore 641114, Tamil Nadu, India

[4] Indian Stat Inst, Syst Sci & Informat Unit, Bangalore 560059, India

[5] Univ Technol & Appl Sci Sur, Coll Appl Sci & Pharm, Dept Appl Biotechnol, POB 484, Sur 411, Oman

[6] Chonnam Natl Univ, Dept Chem & Biomol Engn, Yeosu 59626, Jeonnam, South Korea

[7] Saveetha Inst Med & Tech Sci, Saveetha Dent Coll & Hosp, Dept Biomat, Chennai 600077, Tamil Nadu, India

[8] Annamalai Univ, Dept Chem Engn, Chidambaram 608002, Tamil Nadu, India

[9] Vellore Inst Technol, Sch Adv Sci, Dept Math, Vellore 632014, Tamil Nadu, India

[10] Indian Inst Technol Guwahati, Dept Biosci & Bioengn, Gauhati 781039, Assam, India

[11] IHE Delft Inst Water Educ, Dept Water Supply Sanitat & Environm Engn, POB 3015, NL-2601 DA Delft, Netherlands

来源：

JOURNAL OF ENVIRONMENTAL MANAGEMENT | 2024年 / 370卷

关键词：

Artificial neural network; Machine learning; Air pollution; Wastewater treatment; Resource recovery; Automation and control; NEURAL-NETWORK ANN; BIOLOGICAL HYDROGEN-PRODUCTION; SEQUENCING BATCH REACTOR; FLUIDIZED-BED REACTOR; TREATMENT-PLANT; ANAEROBIC-DIGESTION; MEMBRANE BIOREACTOR; MULTILAYER PERCEPTRON; BIODIESEL PRODUCTION; ORGANIC POLLUTANTS;

D O I：

10.1016/j.jenvman.2024.122386

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The non-linear complex relationships among the process variables in wastewater and waste gas treatment systems possess a significant challenge for real-time systems modelling. Data driven artificial intelligence (AI) tools are increasingly being adopted to predict the process performance, cost-effective process monitoring, and the control of different waste treatment systems, including those involving resource recovery. This review presents an in-depth analysis of the applications of emerging AI tools in physico-chemical and biological processes for the treatment of air pollutants, water and wastewater, and resource recovery processes. Additionally, the successful implementation of AI-controlled wastewater and waste gas treatment systems, along with real-time monitoring at the industrial scale are discussed.

引用

页数：18

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