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Rainfall affects local water quantity and quality. A data-mining approach is applied to predict rainfall in a watershed basin at Oxford, Iowa, based on radar reflectivity and tipping-bucket (TB) data. Five data-mining algorithms, neural network, random forest, classification and regression tree, support vector machine, and k-nearest neighbor, are employed(More)
In wastewater treatment plants, predicting influent water quality is important for energy management. The influent water quality is measured by metrics such as carbonaceous biochemical oxygen demand (CBOD), potential of hydrogen, and total suspended solid. In this paper, a data-driven approach for time-ahead prediction of CBOD is presented. Due to(More)
Total suspended solids (TSS) are a major pollutant that affects waterways all over the world. Predicting the values of TSS is of interest to quality control of wastewater processing. Due to infrequent measurements, time series data for TSS are constructed using influent flow rate and influent carbonaceous bio-chemical oxygen demand (CBOD). We investigated(More)
Sludge is a byproduct of wastewater processing suitable for biogas production. The biogas consisting of about 60% methane can be used to generate electricity and heat. A data-driven approach for optimization of biogas production process in a wastewater treatment plant is presented. The process model is developed using routinely collected data categorized as(More)
A prediction model for methane production in a wastewater processing facility is presented. The model is built by data-mining algorithms based on industrial data collected on a daily basis. Because of many parameters available in this research, a subset of parameters is selected using importance analysis. Prediction results of methane production are(More)
A data-driven approach for maximization of methane production in a wastewater treatment plant is presented. Industrial data collected on a daily basis was used to build the model. Temperature, total solids, volatile solids, detention time and pH value were selected as parameters for the model construction. First, a prediction model of methane production was(More)
Wastewater pumping consumes 10% to 20% of electricity in wastewater treatment plants. To reduce the energy consumption, a data-driven approach is applied to model and optimize the wastewater pumping process. Datamining algorithm and multilayer perceptron neural network are used to build the pumping energy model. The optimization model is solved by an(More)
Recommended Citation Wei, Xiupeng. "Multiscale modeling and simulation of material phase change problems: ice melting and copper crystallization." MS ABSTRACT The primary objective of this work is to propose a state-of-the-art physics based multiscale modeling framework for simulating material phase change problems. Both ice melting and copper(More)
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