Applications of machine learning algorithms for biological wastewater treatment: Updates and perspectives

• Published in: Clean technologies and environmental policy Vol. 23; no. 1; pp. 127 - 143
• Main Authors: Sundui, Batsuren, Ramirez Calderon, Olga Alejandra, Abdeldayem, Omar M., Lázaro-Gil, Jimena, Rene, Eldon R., Sambuu, Uyanga
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.01.2021; Springer Nature B.V
• Subjects: Algae; Algorithms; Bacteria; Biological wastewater treatment; Bioreactors; Circular economy; Consortia; Earth and Environmental Science; Environment; Environmental Economics; Environmental Engineering/Biotechnology; Environmental policy; Fault detection; Flow rates; Flow velocity; Industrial and Production Engineering; Industrial Chemistry/Chemical Engineering; Learning algorithms; Machine learning; Municipal wastewater; Nutrient uptake; Optimization; Original Paper; Pollution; Resource recovery; State-of-the-art reviews; Sustainable Development; Wastewater treatment; Water treatment; Weather forecasting; Bioprocess modelling; Algae–bacteria consortia; Process optimization; Biological wastewater treatment; Machine learning algorithm
• ISSN: 1618-954X; 1618-9558
• DOI: 10.1007/s10098-020-01993-x

Biological wastewater treatment using algae–bacteria consortia for nutrient uptake and resource recovery is a ‘paradigm shift’ from the mainstream wastewater treatment process to mitigate pollution and promote circular economy. The symbiotic relationship between algae and bacteria is complex in open or closed biological wastewater treatment systems. In this regard, machine learning algorithms (MLAs) have found to be advantageous to predict the uncertain performances of the treatment processes. MLAs have shown satisfactory results for effective real-time monitoring, optimization, prediction of uncertainties and fault detection of complex environmental systems. By incorporating these algorithms with online sensors, the transient operating conditions during the treatment process including disruptions or failures due to leaking pipelines, malfunctioning of bioreactors, unexpected fluctuations of organic loadings, flow rate, and temperature can be forecasted efficiently. This paper reviews the state-of-the-art MLA approaches for the integrated operation of biological wastewater treatment systems combining algal biomass production and nutrient recovery from municipal wastewater. Graphic abstract