Energy production through organic fraction of municipal solid waste—A multiple regression modeling approach

Ramesh, N.; Ramesh, S.; Vennila, G.; Bari, J. Abdul; Mageshkumar, P.

doi:10.1016/j.ecoenv.2015.08.027

Cited by 13 publications

(4 citation statements)

References 6 publications

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“…To determine the quality of the prediction model it is necessary to take into account some general guidelines: i) moderation or simplicity; ii) identifiability; iii) goodness of fit; iv) theoretical consistency; and v) predictive power. The specification error was assessed assuming that one or more of the following mistakes were not committed: i) omit a relevant variable; ii) include an unnecessary variable; iii) adopt a wrong functional form; iv) incorrect specification of the stochastic disturbance term; and v) measurement errors [RAMESH et al 2015]. The consequences of including irrelevant variables in a model are, fortunately, not serious.…”

Section: Regression Model Selectionmentioning

confidence: 99%

“…In order to develop the best model, taking into account the highest estimation performance, eight model equations including different input parameter combinations were analysed. RAMESH et al [2015] developed a multiple linear model in which COD removal was the dependent variable, and different parameters, such as HRT, OLR, sludge loading rate, influent pH, effluent pH, inlet and outlet VFA concentration, inlet and outlet volatile suspended solids and total solids (VSS/TS) ratio, and influent and effluent COD, were considered as independent variables. The results of the step-wise regression method applied revealed that only four parameters (influent COD, effluent COD, volatile solids and total solids (VS/TS) ratio and influent pH) were significant on COD removal.…”

Section: Introductionmentioning

confidence: 99%

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Journal of Water and Land Development

Guardia-Puebla,

Llanes-Cedeño,

Domínguez-León

et al. 2021

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A multiple regression model approach was developed to estimate buffering indices, as well as biogas and methane productions in an upflow anaerobic sludge blanket (UASB) reactor treating coffee wet wastewater. Five input variables measured (pH, alkalinity, outlet VFA concentration, and total and soluble COD removal) were selected to develop the best models to identify their importance on methanation. Optimal regression models were selected based on four statistical performance criteria, viz. Mallow's C p statistic (C p ), Akaike information criterion (AIC), Hannan-Quinn criterion (HQC), and Schwarz-Bayesian information criterion (SBIC). The performance of the models selected were assessed through several descriptive statistics such as measure of goodness-of-fit test (coefficient of multiple determination, R 2 ; adjusted coefficient of multiple determination, Adj-R 2 ; standard error of estimation, SEE; and Durbin-Watson statistic, DWS), and statistics on the prediction errors (mean squared error, MSE; mean absolute error, MAE; mean absolute percentage error, MAPE; mean error, ME and mean percentage error, MPE). The estimated model reveals that buffering indices are strongly influenced by three variables (volatile fatty acids (VFA) concentration, soluble COD removal, and alkalinity); while, pH, VFA concentration and total COD removal were the most significant independent variables in biogas and methane production. The developed equation models obtained in this study, could be a powerful tool to predict the functionability and stability for the UASB system.

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Section: Regression Model Selectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Journal of Water and Land Development

Guardia-Puebla,

Llanes-Cedeño,

Domínguez-León

et al. 2021

View full text Add to dashboard Cite

show abstract

“…According to the results, all variables satisfy the Kolmogorow-Smirnov test as well the Shapiro-Wilk Test of normality (p>.05). Therefore, it is allowed to perform the parametric test correlation of Pearson without transforming any variable (Ramesh et al, 2016).…”

Section: The Assumption Of Normalitymentioning

confidence: 99%

Water-Energy-Food nexus status in Brazil

Lisboa

2019

J. Bioen Food Sci

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This article investigates the relationship between the water-energy-food (WEF) nexus in Brazil in an exploratory and statistical study over the period from 2000 to 2013. For that purpose, the explanatory variables considered were the following: Access to improved water; Access to electricity and Average protein supply. Furthermore, some brief facts about WEF nexus security in Brazil was introduced. With such explanatory variables, descriptive statistics was performed, ensuring the assumption of data normality approach, and consequently calculating the descriptive statistics and correlation. The results of correlation showed that the quantitative relation between the three factors was extremely strong. These data allowed to understand the relation between these indicators and its status as a nexus in Brazil. Therefore, it is concluded that pursuing an active policy to manage demand in WEF nexus could lead sustainability processes.

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“…Data-driven models use artificial intelligence; for example, back propagation (BP) neural networks apply self-learning to existing data to deduct an optimal model (Yu et al, 2013). Factor models are based on factors that help in the prediction of waste generation, but also allow researchers to quantify factors' contribution to the model (Ramesh et al, 2015). Conventional waste prediction models often include correlation analysis and consider various independent variables (Kolekar et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Hybrid model for the prediction of municipal solid waste generation in Hangzhou, China

Zhang

2019

Waste Manag Res

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Accurate prediction of municipal solid waste (MSW) generation is necessary for choosing appropriate waste treatment methods and for planning the distribution of disposal facilities. In this study, a hybrid model was established to forecast MSW generation through the combination of the ridge regression and GM(1,N) models. The hybrid model is multivariate and involves total urban population, total retail sales of social consumer goods, per capita consumption expenditure of urban areas, tourism, and college graduation. Compared with the constituent models alone, the hybrid model yields higher accuracy, with a mean absolute percentage error (MAPE) of only 2.59%. Through weight allocation and optimal treatment of residuals, the hybrid model also balances the growth trends of the individual models, making the prediction curve smoother. The model coefficients and correlation analysis show that population, economics, and educational factors are influential for waste generation. MSW output in Hangzhou will gradually increase in the future, and is expected to reach 5.12 million tons in 2021. Results can help decision makers to develop the measures and policies of waste management in the future.

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Energy production through organic fraction of municipal solid waste—A multiple regression modeling approach

Cited by 13 publications

References 6 publications

Journal of Water and Land Development

Journal of Water and Land Development

Water-Energy-Food nexus status in Brazil

Hybrid model for the prediction of municipal solid waste generation in Hangzhou, China

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