Electrodialytic desalination of brackish water: determination of optimal experimental parameters using full factorial design

Mansoor

2019

OPSEARCH

In this work, we propose a hybrid regression model to solve a specific problem faced by a modern paper manufacturing company. Boiler inlet water quality is a major concern for the paper machine. If water treatment plant can not produce water of desired quality, then it results in poor health of the boiler water tube and consequently affects the quality of the paper. This variation is due to several crucial process parameters. We build a hybrid regression model based on regression tree and support vector regression for boiler water quality prediction and show its excellent performance as compared to other state-of-the-art.

Section: Introductionmentioning

confidence: 99%

A hybrid regression model for water quality prediction

Mansoor

2019

OPSEARCH

“…The problem can be viewed as a typical nonparametric regression problems where one can establish a relationship between the response variable (recovery percentage of FFRE) and the major causal variables (process parameters of DAFSP) without having any prior information about the data. Regression trees (RTs), support vector regression (SVR), and artificial neural networks (ANNs) have been applied for various prediction tasks in water quality improvement, water planning, and many other related problems . Even various hybrid regression models have been developed for performing regression task in many real‐life problems such as water demand forecasting and others .…”

Section: Introductionmentioning

confidence: 99%

“…Regression trees (RTs), support vector regression (SVR), and artificial neural networks (ANNs) have been applied for various prediction tasks in water quality improvement, water planning, and many other related problems. [6][7][8][9] Even various hybrid regression models have been developed for performing regression task in many real-life problems such as water demand forecasting and others. [10][11][12][13] To develop a prediction model for the waste recovery improvement that can also find out critical parameters among the set of possible parameters, we take recourse to nonparametric regression methodology.…”

Section: Introductionmentioning

confidence: 99%

Radial basis neural tree model for improving waste recovery process in a paper industry

Chattopadhyay

Appl Stoch Models Bus & Ind

2019

In this article, we propose a novel hybridization of regression trees (RTs) and radial basis function networks, namely, radial basis neural tree model, for waste recovery process (WRP) improvement in a paper industry. As a by‐product of the paper manufacturing process, a lot of waste along with valuable fibers and fillers come out from the paper machine. The WRP involves separating the unwanted materials from the valuable ones so that the recovered fibers and fillers can be further reused in the production process. This job is done by fiber‐filler recovery equipment (FFRE). The efficiency of FFRE depends on several crucial process parameters, and monitoring them is a difficult proposition. The proposed model can be useful to find the essential parameters from the set of available data and to perform prediction task to improve WRP efficiency. An idea of parameter optimization along with regularity conditions for the universal consistency of the proposed model is given. The proposed model has the advantages of easy interpretability and excellent performance when applied to the FFRE efficiency improvement problem. Improved waste recovery will help the industry to become environmentally friendly with less ecological damage apart from being cost‐effective.

“…The problem can be viewed as a typical nonparametric regression problems where one can establish a relationship between the response variable (recovery percentage of FFRE) and the major causal variables (process parameters of DAFSP) without having any prior information about the data. Regression trees (RT), support vector regression (SVR) and artificial neural networks (ANN) have been applied for various prediction tasks in water quality improvement, water planning and many other related problems 3,4,5,6 . Even various hybrid regression models have been developed for performing regression task in many real-life problems like water demand forecasting and others 7,8,9,10 .…”

Section: Introductionmentioning

confidence: 99%

Radial basis neural tree model for improving waste recovery process in a paper industry

Chakraborty¹,

Chattopadhyay²,

Chakraborty³

2018

Preprint

In this article, we propose a novel hybridization of regression trees (RT) and radial basis function networks (RBFN), namely, radial basis neural tree (RBNT) model,for waste recovery process improvement in the paper industry. As a by-product of the paper manufacturing process, a lot of waste along with valuable fibers and fillerscome out from the paper machine. The waste recovery process (WRP) involves separating the unwanted materials from the valuable ones so that the recovered fibersand fillers can be further reused in the production process. This job is done by fiber-filler recovery equipment (FFRE). The efficiency of FFRE depends on severalcrucial process parameters and monitoring them is a difficult proposition. The proposed model can be useful to find the essential parameters from the set of availabledata and perform prediction task to improve waste recovery process efficiency. An idea of parameter optimization along with regularity conditions for the universal consistency of the proposed model are given. The proposed model has the advantages of easy interpretability and excellent performance when applied to the FFREefficiency improvement problem. Improved waste recovery will help the industry to become environmentally friendly with less ecological damage apart from being cost-effective.