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Comparison of the Prediction Accuracy of Total Viable Bacteria Counts in a Batch Balloon Digester Charged with Cow Manure: Multiple Linear Regression and Non-Linear Regression Models
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Comparison of the Prediction Accuracy of Total Viable Bacteria Counts in a Batch Balloon Digester Charged with Cow Manure: Multiple Linear Regression and Non-Linear Regression Models
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Comparison of the Prediction Accuracy of Total Viable Bacteria Counts in a Batch Balloon Digester Charged with Cow Manure: Multiple Linear Regression and Non-Linear Regression Models
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Journal Article
Comparison of the Prediction Accuracy of Total Viable Bacteria Counts in a Batch Balloon Digester Charged with Cow Manure: Multiple Linear Regression and Non-Linear Regression Models
2022
Overview
Biogas technology is rapidly gaining market penetration, and the type of digesters employed in the harnessing of the biogas from biodegradable waste is crucial in enhancing the total viable bacteria counts. This study focused on the exploration of input parameter (number of days, daily slurry temperature, and pH) and target (total viable bacteria counts) datasets from anaerobic balloon digester charged with cow manure using data acquisition system and standard methods. The predictors were ranked according to their weights of importance to the desired targets using the reliefF test. The complete dataset was randomly partitioned into testing and validated samples at a ratio of 60% and 40%, respectively. The developed non-linear regression model applied on the testing samples was capable of predicting the yield of the total viable bacteria counts with better accuracy as the determination coefficient, mean absolute error, and p-value were 0.959, 0.180, and 0.602, respectively, as opposed to the prediction with the multiple linear regression model that yielded 0.920, 0.206, and 0.514, respectively. The 2D multi-contour surface plots derived from the developed models were used to simulate the variation in the desired targets to each predictor while the others were held constant.
Publisher
MDPI AG
Subject
