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Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1)

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Abstract

In the past decade, machine learning techniques have seen wide industrial applications for design of data-based process monitoring systems with an aim to improve industrial productivity. An efficient process monitoring system for wastewater treatment process (WWTP) ensures increased efficiency and effluents meeting stringent emission norms. Benchmark simulation model No. 1 (BSM1) provides a simulation platform to researchers for developing efficient data-based process monitoring, quality monitoring, and process control systems for WWTPs. The present article presents a review of all research works reporting applications of various machine learning techniques for sensor and process fault detection of BSM1. The review focuses on process monitoring of biological wastewater treatment process, which uses a series of aerobic and anaerobic reactions followed by secondary settling process. Detailed information on various parameters monitored, different machine learning techniques explored, and results obtained by different researchers are presented in tabular and graphical format. In the review, it was observed that principal component analysis (PCA) and its variants account for the maximum number of research works for process monitoring in WWTPs and there are very few applications of recently developed deep learning techniques. Following the review and analysis, various future scopes of research (such as techniques yet to be explored or improvement of results for a particular fault) are also presented. These information will assist prospective researchers working on BSM1 to take forward the research.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

xCOD :

Concentration of COD in effluent flow

xS_S5 :

Concentration of readily biodegradable substrate in the 5th reactor

xS_O5 :

Concentration of dissolved oxygen in the 5th reactor

xS_NO5 :

Concentration of nitrate and nitrite nitrogen in the 5th reactor

xS_NH5 :

Concentration of NH4 +  + NH3 nitrogen in the 5th reactor

xS_ALK5 :

Alkalinity in the 5th reactor

xQ_f :

Flow rate entering the clarifier

BOD:

Biological oxygen demand

xQ_a :

Flow rate of internal reflux

A,c,l,p:

Whale variables

Scin :

The concentration of substrate in the inflow

Soin :

The concentration of dissolved oxygen in the inflow

qin :

The inflow rate

Sos :

The dissolved oxygen mass at saturation

k1 :

The conversion coefficient of the substrate to biomass

k2 :

The conversion coefficient of oxygen to the biomass

b:

The endogenous respiration coefficient

kLa :

The oxygen mass transfer coefficient

Ki:

Inhibition coefficient

Ks:

The half-saturation coefficient

XCOD :

Chemical oxygen demand concentration

SO :

Dissolve oxygen concentration

xB, H :

Active heterotrophic biomass

xSNH :

Ammonia concentration

xSNO :

Nitrate concentration

xB, A :

Active autotrophic biomass

xND :

Particulate biodegradable organic nitrogen

SND :

Soluble biodegradable organic nitrogen

SSe:

Suspended solids concentration in the effluent

SSin:

Suspended solids concentration in the influent

xS :

Biodegradable substrate

TN:

Total nitrogen concentration in the effluent

TCOD:

Total chemical oxygen demand in the effluent

rp :

Flocculent zone settling parameter

µA :

Autotrophic bacteria growth rate

Qr:

Internal return flow change

µH :

Maximum heterotrophic growth rate

Qw:

Sludge discharge flow rate

AC:

Ammonia concentration

v0 :

Clarifier parameter

TSS:

Total suspended solid concentration

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  1. Department of Chemical Engineering, Birla Institute of Technology and Science, Pilani, Rajasthan, India, 333031

    Amir Khurshid & Ajaya Kumar Pani

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Mr. Amir Khurshid and Dr. Ajaya Kumar Pani have jointly prepared this review article. Mr. Khurshid has collected all the relevant information from the published literature and prepared the tables and graphs in the first, second, and third sections. Abstract and the first, fourth, and fifth sections are written by Dr. Pani.

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Correspondence to Ajaya Kumar Pani.

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Khurshid, A., Pani, A.K. Machine learning approaches for data-driven process monitoring of biological wastewater treatment plant: A review of research works on benchmark simulation model No. 1(BSM1). Environ Monit Assess 195, 916 (2023). https://doi.org/10.1007/s10661-023-11463-8

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