Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions

© 2020 Elsevier Ltd Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid waste under different vermicompost treatments. Seven chemical and biological indices were studied as input variables to predict total nitrogen (TN) and total phosphorus (TP) recovery. The developed ANN and MLR models were compared by statistical analysis including R-squared (R2), Adjusted-R2, Root Mean Square Error and Absolute Average Deviation. The results showed that vermicomposting increased TN and TP proportions in final products by 1.5 and 16 times. The ANN models provided better prediction for TN and TP with R2 of 0.9983 and 0.9991 respectively, compared with MLR models with R2 of 0.834 and 0.729. TN and C/N ratio were key factors for TP and TN prediction by ANN with percentages of 17.76 and 18.33

Improving formaldehyde removal from water and wastewater by fenton, photo-fenton and ozonation/fenton processes through optimization and modeling

This study aimed to assess, optimize and model the efficiencies of Fenton, photo-Fenton and ozonation/Fenton processes in formaldehyde elimination from water and wastewater using the response surface methodology (RSM) and artificial neural network (ANN). A sensitivity analysis was used to determine the importance of the independent variables. The influences of different variables, including H2O2 concentration, initial formaldehyde concentration, Fe dosage, pH, contact time, UV and ozonation, on formaldehyde removal efficiency were studied. The optimized Fenton process demonstrated 75% formaldehyde removal from water. The best performance with 80% formaldehyde removal from wastewater was achieved using the combined ozonation/Fenton process. The developed ANN model demonstrated better adequacy and goodness of fit with a R2 of 0.9454 than the RSM model with a R2 of 0. 9186. The sensitivity analysis showed pH as the most important factor (31%) affecting the Fenton process, followed by the H2O2 concentration (23%), Fe dosage (21%), contact time (14%) and formaldehyde concentration (12%). The findings demonstrated that these treatment processes and models are important tools for formaldehyde elimination from wastewater

A Study Identifying Causes of Construction Waste Production and Applying Safety Management on Construction Site A Study Identifying Causes of Construction Waste Production and Applying Safety Management on Construction Site

Abstract Background and purpose: In a recent century, the amount of construction waste has increased significantly. Although the building industry has a considerable role in the development of a society, it is regarded as an environmentally destructive. Source reduction is the highest goal in the waste management hierarchy and is in priority. It also has economic benefits by reducing costs associated with transportation, disposal or recycling of wastes. The present study is aimed to identify activities generating the wastes in design, transportation and storage and procurement of building materials. Materials and Methods: This was questionnaire survey. A total of 94 professionals in the construction industry were attended in this study. To determine the validity and reliability of the instrument, content validity method and Cronbach's alpha coefficient (0.79) were used. Data were analyzed using SPSS for Windows. Frequencies, percentage, mean and standard deviation were determined in this research. Results: The results showed that handling and storage have been chosen as the most causative factor of waste production in construction activity. Improper material storage was identified major factor in producing waste in handling and storage phase. Usage of low-quality material in design stage and material price changes in procurement were recognized as major causes of waste production in these stages. Conclusion: All studied phases in this research were identified as causative factors in producing of waste. Identifying causes of construction waste production will help us decide better how to control this sort of wastes