A machine learning framework to improve effluent quality control in wastewater treatment plants

Wang, Dong; Thunéll, Sven; Lindberg, Ulrika; Jiang, Lili; Trygg, Johan; Tysklind, Mats; Souihi, Nabil

doi:10.1016/j.scitotenv.2021.147138

Cited by 116 publications

(46 citation statements)

References 53 publications

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“…Hence, the phytotoxicity assessment on crop plants was carried out further to see the impact of treatment efficiency as predicted by our RF classifier. In light of the standard methods for wastewater quality analysis, five wastewater quality predicting parameters have been used in machine learning, i.e., BOD [14], COD [41,[54][55][56], TSS [41,52,56], TDS [41] and turbidity [57,58]. The present RF classification successfully used artificial intelligence to predict twelve quality parameters between wastewater that was treated with the bacterial consortium versus untreated wastewater.…”

Section: Prediction Of Quality Parametersmentioning

confidence: 99%

Machine Learning Approach to Predict Quality Parameters for Bacterial Consortium-Treated Hospital Wastewater and Phytotoxicity Assessment on Radish, Cauliflower, Hot Pepper, Rice and Wheat Crops

Rashid

Mirza

Keating

et al. 2022

Water

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Raw hospital wastewater is a source of excessive heavy metals and pharmaceutical pollutants. In water-stressed countries such as Pakistan, the practice of unsafe reuse by local farmers for crop irrigation is of major concern. In our previous work, we developed a low-cost bacterial consortium wastewater treatment method. Here, in a two-part study, we first aimed to find what physico-chemical parameters were the most important for differentiating consortium-treated and untreated wastewater for its safe reuse. This was achieved using a Kruskal–Wallis test on a suite of physico-chemical measurements to find those parameters which were differentially abundant between consortium-treated and untreated wastewater. The differentially abundant parameters were then input to a Random Forest classifier. The classifier showed that ‘turbidity’ was the most influential parameter for predicting biotreatment. In the second part of our study, we wanted to know if the consortium-treated wastewater was safe for crop irrigation. We therefore carried out a plant growth experiment using a range of popular crop plants in Pakistan (Radish, Cauliflower, Hot pepper, Rice and Wheat), which were grown using irrigation from consortium-treated and untreated hospital wastewater at a range of dilutions (turbidity levels) and performed a phytotoxicity assessment. Our results showed an increasing trend in germination indices and a decreasing one in phytotoxicity indices in plants after irrigation with consortium-treated hospital wastewater (at each dilution/turbidity measure). The comparative study of growth between plants showed the following trend: Cauliflower > Radish > Wheat > Rice > Hot pepper. Cauliflower was the most adaptive plant (PI: −0.28, −0.13, −0.16, −0.06) for the treated hospital wastewater, while hot pepper was susceptible for reuse; hence, we conclude that bacterial consortium-treated hospital wastewater is safe for reuse for the irrigation of cauliflower, radish, wheat and rice. We further conclude that turbidity is the most influential parameter for predicting bio-treatment efficiency prior to water reuse. This method, therefore, could represent a low-cost, low-tech and safe means for farmers to grow crops in water stressed areas.

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Section: Prediction Of Quality Parametersmentioning

confidence: 99%

Machine Learning Approach to Predict Quality Parameters for Bacterial Consortium-Treated Hospital Wastewater and Phytotoxicity Assessment on Radish, Cauliflower, Hot Pepper, Rice and Wheat Crops

Rashid

Mirza

Keating

et al. 2022

Water

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“…Sometimes another model (e.g. ANN) could be used for the validation of RF model (Wang et al, 2021), however, RF has its advantages over other methods (e.g., importance of every input variable) as the representativeness of training data highly affects model performance (Salem et al, 2022). (Bagherzadeh et al, 2021)…”

Section: Rf Modelmentioning

confidence: 99%

“…Some inorganic ceramics (e.g., zirconium phosphate, zeolite) are also combined with silver (Wang et al, 2021) thus are able to trap metal ions and may then be added to other materials (e.g., paints, plastics, waxes, polyesters) to confer antimicrobial properties (Asafa et al, 2021;Kurnyta et al, 2021;Ma et al, 2016). Among these materials, silver nanoparticles are increasingly being used as a comprehensive antimicrobial agent in clothing, food storage containers, pharmaceuticals, cosmetics, electronics, and optical devices.…”

Section: Introductionmentioning

confidence: 99%

Proceedings - The Eleventh International Symposium GRID 2022

2022

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It is my great pleasure to introduce You the research papers of the Eleventh Symposium on Graphic Engineering and Design.With this proceeding we continue the works of previous symposiums which have been held biennial since year 2002.We're delighted that this international symposium has once again gatered a great number of participants from many countries as it became a tradition over the past 20 years.The papers include the achievements of researches in the field of technology and scientific areas relevant to graphic technology and graphic design. Through the work of the symposium GRID we continued significant scientific cooperation with educational institutions all over the world. With them we are continuing good cooperation which is the driving force for the creation and display of new developments, both individual and common. I want to thank everyone who participated with their paper and presentation in the Symposium. Your contribution is significant for the improvement of the Symposium on Graphic Engineering and Design GRID 22. The research achievements presented here are also valuable to the scientific and professional community and are highly appreciated.

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“…It can be used to gain insight; detect and respond to process failures, inefficiencies, and abnormalities; and optimize operations (Asadi et al, 2017;Newhart et al, 2019). Machine learning algorithms, such as random forest (RF) and support vector machine (SVM), and deeplearning algorithms, such as artificial neural network (ANN), have demonstrated utility in the water sector (R aduly et al, 2007;Wang et al, 2021). Mulrow et al (2020) used RF to develop a 3-day advanced warning predictor of local odor complaints at a large stormwater reservoir.…”

Section: Introductionmentioning

confidence: 99%

Comparison of random forest and multiple linear regression to model the mass balance of biosolids from a complex biosolids management area

Pluth

Brose

2021

Water Environment Research

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The use of biosolids as a soil amendment provides an important alternative to disposal and can improve soil health; however, distribution for water resource recovery facilities (WRRFs) in the United States can be challenging due to decreasing cropland, increased precipitation, variable plant operations, and financial constraints. Although statistical modeling is commonly used in the water sector, machine learning is still an emerging tool and can provide insights to optimize operations. Random forest (RF), a machine learning model, and multiple linear regression (MLR) were used in this study to model the mass balance of biosolids from a complex biosolids management area. The RF model outperformed (R 2 = 0.89) the MLR model (R 2 = 0.49) and showed that rainfall was a major factor impacting distribution. Storage for dried biosolids would help decouple drying operations from wet weather and increase distribution. This study demonstrated how machine learning can assist in decision-making processes for long-term planning at WRRFs. Practitioner Points• Random forest predicted the 7-day average mass balance of biosolids from a complex biosolids management area.• Decoupling biosolids drying operations from wet weather was identified as the highest operational priority.• Machine learning outperformed multiple linear regression and can be an important tool for the water sector.

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A machine learning framework to improve effluent quality control in wastewater treatment plants

Cited by 116 publications

References 53 publications

Machine Learning Approach to Predict Quality Parameters for Bacterial Consortium-Treated Hospital Wastewater and Phytotoxicity Assessment on Radish, Cauliflower, Hot Pepper, Rice and Wheat Crops

Machine Learning Approach to Predict Quality Parameters for Bacterial Consortium-Treated Hospital Wastewater and Phytotoxicity Assessment on Radish, Cauliflower, Hot Pepper, Rice and Wheat Crops

Proceedings - The Eleventh International Symposium GRID 2022

Comparison of random forest and multiple linear regression to model the mass balance of biosolids from a complex biosolids management area

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