Biochemical Oxygen Demand Soft Measurement Based On LE-RVM

Luo, Long

doi:10.2991/icsd-16.2017.35

Cited by 2 publications

(3 citation statements)

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“…Examples of methodology used in these approaches are principal components regression [32], artificial neural network [33], neuro-fuzzy systems [34], ML algorithms [35] like IBK, random forest, random tree, Kstar, REPTree, support vector machine (SVM) [21,36], and Gaussian processes [37,38]. The soft sensor concept is now widely being used in different application areas, such as biological wastewater treatment [19], bioprocess monitoring [29], bio-chemical systems [39], and many complex process predictions [16,30].…”

Section: Related Workmentioning

confidence: 99%

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Machine learning based soft sensor model for BOD estimation using intelligence at edge

Pattnaik

Pattanayak

Udgata

et al. 2021

Complex Intell. Syst.

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Real-time water quality monitoring is a complex system as it involves many quality parameters to be monitored, the nature of these parameters, and non-linear interdependence between themselves. Intelligent algorithms crucial in building intelligent systems are good candidates for building a reliable and convenient monitoring system. To analyze water quality, we need to understand, model, and monitor the water pollution in real time using different online water quality sensors through an Internet of things framework. However, many water quality parameters cannot be easily measured online due to several reasons such as high-cost sensors, low sampling rate, multiple processing stages by few heterogeneous sensors, the requirement of frequent cleaning and calibration, and spatial and application dependency among different water bodies. A soft sensor is an efficient and convenient alternative approach for water quality monitoring. In this paper, we propose a machine learning-based soft sensor model to estimate biological oxygen demand (BOD), a time-consuming and challenging process to measure. We also propose a system architecture for implementing the soft sensor both on the cloud and edge layers, so that the edge device can make adaptive decisions in real time by monitoring the quality of water. A comparative study between the computational performance of edge and cloud nodes in terms of prediction accuracy, learning time, and decision time for different machine learning (ML) algorithms is also presented. This paper establishes that BOD soft sensors are efficient, less costly, and reasonably accurate with an example of a real-life application. Here, the IBK ML technique proves to be the most efficient in predicting BOD. The experimental setup uses 100 test readings of STP water samples to evaluate the performance of the IBK technique, and the statistical measures are reported as correlation coefficient = 0.9273, MAE = 0.082, RMSE = 0.1994, RAE = 17.20%, RRSE = 37.62%, and edge response time = 0.15 s only.

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Section: Related Workmentioning

confidence: 99%

“…This soft sensor model was used to predict the BOD concentration. Luo [39] proposed an online soft BOD measurement method based on Laplacian Eigenmapsrelevance vector machine (LE-RVM). LE technique was used to process the pre-processed parameters and then is applied as the input of SVM to build the BOD soft sensor model.…”

Section: Related Workmentioning

confidence: 99%

Machine learning based soft sensor model for BOD estimation using intelligence at edge

Pattnaik

Pattanayak

Udgata

et al. 2021

Complex Intell. Syst.

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“…To avoid the above problems, the soft measurement method provides a new way to predict BOD concentration in recent years, which uses easy-to-measure variables to predict hard-to-measure variables and has applied widely in WWTP [7][8][9]. With the development of artificial intelligent technology, Appl.…”

Section: Introductionmentioning

confidence: 99%

Prediction of BOD Concentration in Wastewater Treatment Process Using a Modular Neural Network in Combination with the Weather Condition

Zhang

2020

Applied Sciences

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Since weather has a huge impact on the wastewater treatment process (WWTP), the prediction accuracy for the Biochemical Oxygen Demand (BOD) concentration in WWTP would degenerate if using only one single artificial neural network as the model for soft measurement method. Aiming to solve this problem, the present study proposes a novel hybrid scheme using a modular neural network (MNN) combining with the factor of weather condition. First, discriminative features among different weather groups are selected to ensure a high accuracy for sample clustering based on weather conditions. Second, the samples are clustered based on a density-based clustering algorithm using the discriminative features. Third, the clustered samples are input to each module in MNN, with the auxiliary variables correlated with BOD prediction input to the corresponding model. Finally, a constructive radial basis function neural network with the error-correction algorithm is used as the model for each subnetwork to predict BOD concentration. The proposed scheme is evaluated on a standard wastewater treatment platform—Benchmark Simulation Model 1 (BSM1). Experimental results demonstrate the performance improvement of the proposed scheme on the prediction accuracy for BOD concentration in WWTP. Besides, the training time is shortened and the network structure is compact.

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Biochemical Oxygen Demand Soft Measurement Based On LE-RVM

Cited by 2 publications

References 5 publications

Machine learning based soft sensor model for BOD estimation using intelligence at edge

Machine learning based soft sensor model for BOD estimation using intelligence at edge

Prediction of BOD Concentration in Wastewater Treatment Process Using a Modular Neural Network in Combination with the Weather Condition

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