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Prithvi Sai Nadh Garikiparthy,유창규,이승철,류홍빈,Srinivas Sahan Kolluri,Iman Janghorban Esfahani 한국화학공학회 2016 Korean Journal of Chemical Engineering Vol.33 No.1
We developed several control algorithms and compare their control performances for controlling the total phosphorous (TP) concentration in wastewater treatment plant, which has strong influent disturbances and the disturbance effects should be removed while maintaining better effluent quality. An anaerobic - anoxic - oxic (AAO) process, which is a well-known advanced nutrient removal process, was selected as a case study, which is modeled with activated sludge model no. 2. Six control strategies for TP control with a polymer addition were implemented in AAO process and evaluated by the plant’s performance, where the costs of the dosed chemical were compared among the six controllers. The experimental work showed that the advanced control techniques with feedback, feedforward and feedratio controllers were able to control the TP concentration in the effluent, which must be less than 1.50 g P/m3 which is the legal limitation, while reducing the necessary chemical cost. The results showed that the best TP removal performance in the effluent TP removal could be achieved by advanced feedback controller with the tuned control parameters, which showed the best effluent quality and control performance index as well as the cheapest cost of chemical dosage among the six TP control strategies.
Srinivas Sahan Kolluri,Iman Janghorban Esfahani,Prithvi Sai Nadh Garikiparthy,유창규 한국화학공학회 2015 Korean Journal of Chemical Engineering Vol.32 No.8
Our aim was to analyze, monitor, and predict the outcomes of processes in a full-scale seawater reverse osmosis (SWRO) desalination plant using multivariate statistical techniques. Multivariate analysis of variance (MANOVA) was used to investigate the performance and efficiencies of two SWRO processes, namely, pore controllable fiber filterreverse osmosis (PCF-SWRO) and sand filtration-ultra filtration-reverse osmosis (SF-UF-SWRO). Principal component analysis (PCA) was applied to monitor the two SWRO processes. PCA monitoring revealed that the SF-UF-SWRO process could be analyzed reliably with a low number of outliers and disturbances. Partial least squares (PLS) analysis was then conducted to predict which of the seven input parameters of feed flow rate, PCF/SF-UF filtrate flow rate, temperature of feed water, turbidity feed, pH, reverse osmosis (RO)flow rate, and pressure had a significant effect on the outcome variables of permeate flow rate and concentration. Root mean squared errors (RMSEs) of the PLS models for permeate flow rates were 31.5 and 28.6 for the PCF-SWRO process and SF-UF-SWRO process, respectively, while RMSEs of permeate concentrations were 350.44 and 289.4, respectively. These results indicate that the SF-UF-SWRO process can be modeled more accurately than the PCF-SWRO process, because the RMSE values of permeate flowrate and concentration obtained using a PLS regression model of the SF-UF-SWRO process were lower than those obtained for the PCF-SWRO process.