Xiupeng Wei

Predicting the total suspended solids in wastewater: A data-mining approach

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 different scenarios of daily average influent CBOD and influent flow rate measured at 15min intervals. Then, we used five data-mining algorithms, i.e., multi-layered perceptron, k-nearest neighbor, multi-variate adaptive regression spline, support vector machine, and random forest, to construct day-ahead, time-series prediction models for TSS. Historical TSS values were used as input parameters to predict current and future values of TSS. A sliding-window approach was used to improve the results of the predictions.

Modeling and Prediction of Rainfall Using Radar Reflectivity Data: A Data-Mining Approach

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 to build prediction models. The algorithm offering the highest accuracy is selected for further study. Model I is the baseline model constructed from radar data covering Oxford. Model II predicts rainfall from radar and TB data collected at Oxford. Model III is constructed from the radar and TB data collected at South Amana (16 km west of Oxford) and Iowa City (25 km east of Oxford). The computation results indicate that the three models offer similar accuracy when predicting rainfall at current time. Model II performs better than the other two models when predicting rainfall at future time horizons.

Prediction of Influent Flow Rate: Data-Mining Approach

In this paper, models for short-term prediction of influent flow rate in a wastewater-treatment plant are discussed. The prediction horizon of the model is up to 180 min. The influent flow rate, rainfall rate, and radar reflectivity data are used to build the prediction model by different data-mining algorithms. The multilayer perceptron neural network algorithm has been selected to build the prediction models for different time horizons. The computational results show that the prediction model performs well for horizons up to 150 min. Both the peak values and the trends are accurately predicted by the model. There is a small lag between the predicted and observed influent flow rate for horizons exceeding 30 min. The lag becomes larger with the increase of the prediction horizon. DOI: 10.1061/(ASCE)EY.1943-7897 .0000103.

Prediction of methane production in wastewater treatment facility: a data-mining approach

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 presented in this paper. The model performance by different algorithms is measured with five metrics. Based on these metrics, a model built by the Adaptive Neuro-Fuzzy Inference System algorithm has provided most accurate predictions of methane production.

A data-driven model for maximization of methane production in a wastewater treatment plant.

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 built by a multi-layer perceptron neural network. Then a particle swarm optimization algorithm was used to maximize methane production based on the model developed in this research. The model resulted in a 5.5% increase in methane production.

Optimizing wastewater pumping system with data-driven models and a greedy electromagnetism-like algorithm

Abstract Optimizing a pumping system in the wastewater treatment process by improving its operational schedules is presented. The energy consumption and outflow rate of the pumping system are modeled by a data-driven approach. A mixed-integer nonlinear programming (MINLP) model containing data-driven components and pump operational constraints is developed to minimize the energy consumption of the pumping system while maintaining the required pumping workload. A greedy electromagnetism-like (GEM) algorithm is designed to solve the MINLP model for optimized operational schedules and pump speeds. Three computational cases are studied to demonstrate the effectiveness of the proposed data-driven modeling and GEM algorithm. The computational results show that significant energy saving can be obtained.