Investigating regime shifts and the factors controlling Total Inorganic Nitrogen concentrations in treated wastewater using non-homogeneous Hidden Markov and multinomial logistic regression models

Electrocoagulation Based Chromium Removal Efficiency Classification Using Logistic Regression

“…The same last method was used to reduce the number of entities and to create a classifier with a higher accuracy rate than all the entities. Also apply in water treatment, for instance there is an article [16] controlled total inorganic nitrogen in treated wastewater using non homogeneous Markov logistic and multinomial logistic regression models. The results of this study indicate that temperatures have been cooled, the total ammonia nitrogen (TAN) in the effluents and the TIN levels in the effluents from previous weeks predict the TIN concentrations in the effluents.…”

Design of a Soft Sensor for Monitoring Phosphorous Uptake in an EBPR Process

“…Machine learning (ML) approaches have proven useful as a data fusion technique when using sensor arrays in complex environments, improving the ability to capturing (usually nonlinear) underline patterns in spite of (often covarying) interferences. Due to the often high cost of collecting training data in environmental systems, algorithms with a small number of tuning parameters, that is, which can be trained using a relatively small dataset, ,− are of particular interest. For example, support vector machines (SVM), a nonlinear regression method, has been successfully applied in various environmental studies including Earth surface classification from LIDAR data and water quality classification for planning and operational policies .…”

“…For example, support vector machines (SVM), a nonlinear regression method, has been successfully applied in various environmental studies including Earth surface classification from LIDAR data and water quality classification for planning and operational policies . Logistic regression (LR), which belongs to the family of generalized linear models, has been used to identify factors driving wastewater effluent total inorganic nitrogen (TIN) concentration and to improve process control and fouling risk in WWT . Random forest (RF), a tree-based method, has also been used to identify factors driving wastewater effluent TIN concentration and to improve process control and fouling risk in WWT .…”

“…Logistic regression (LR), which belongs to the family of generalized linear models, has been used to identify factors driving wastewater effluent total inorganic nitrogen (TIN) concentration and to improve process control and fouling risk in WWT . Random forest (RF), a tree-based method, has also been used to identify factors driving wastewater effluent TIN concentration and to improve process control and fouling risk in WWT . Finally, Bayesian classification (BC), a probabilistic classifier that uses Bayes’ theorem to predict the class of an observation, has been demonstrated in environmental applications for monitoring small reservoirs from radar satellite images …”

Enhanced stability and nitrogen removal efficiency of Klebsiella sp. entrapped in chitosan beads applied in the domestic sewage system

“…1 For decades, excessive inorganic nitrogen generated from human activities has been discharged into the aquatic environment, resulting in adverse effects on the environment 2 and human health. [3][4][5] Therefore, lowering nitrogen levels in wastewater is important and necessary. Biological nitrogen removal is regarded as the most common, efficient, and cost-effective method for wastewater treatment owing to the critical biochemical process for converting nitrogenous compounds to nitrogen gas.…”