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Performance assessment of cascade controllers for nitrate control in a wastewater treatment process
류홍빈,유창규 한국화학공학회 2011 Korean Journal of Chemical Engineering Vol.28 No.3
A cascade control strategy is proposed to the benchmark simulation model 1 (BSM1) to enhance the treatment performance of nitrogen removal in a biological wastewater treatment plant. The proposed control approach consists of two control loops, a primary outer loop and a secondary inner loop. The method has two controllers of which the primary loop has a model predictive control (MPC) controller and the secondary loop has a proportional-integralderivative (PID) controller, which is a cascade MPC_PID controller. The primary MPC controller is to control the nitrate concentration in the effluent, and the secondary PID controller is to control the nitrate concentration in the final anoxic compartment. The proposed method controls the nitrate concentrations in the effluent as well as in the final anoxic reactor simultaneously to strictly satisfy the quality of the effluent as well as to remove the effects of disturbances more quickly by manipulating the external carbon dosage rate. Because the control performance assessment (CPA) technique has the features of determining the capability of the current controller and locating the best achievable performance,the other novelty of this paper is to suggest a relative closed-loop potential index (RCPI) which updates the CPA technology into a closed-loop cascade controller. The proposed method is compared with a cascade PID-PID control strategy and the original PID controller in BSM1 and an improved performance of the suggested cascade MPC-PID controller is obtained by using the CPA approach.
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유창규,류홍빈,Mingzhi Huang,김정태 한국화학공학회 2013 Korean Journal of Chemical Engineering Vol.30 No.3
A new faulty sensor monitoring method based on an adaptive neuro-fuzzy inference system (ANFIS) is proposed to improve the monitoring performance of indoor air quality (IAQ) in subway stations. To enhance network performance, a data preprocessing step for detecting outliers and treating missing data is implemented before building the monitoring models. A squared prediction error (SPE) monitoring index based on the ANFIS prediction model is proposed to detect sensor faults, where the confidence limit for the SPE index is determined by using the kernel density estimation method. The proposed monitoring approach is applied to detect four typical kinds of sensor faults that may happen in the indoor space of a subway. The prediction results in the subway system indicate that the prediction accuracy of an ANFIS structure with 15 clusters is superior to that of an appropriate artificial neural network structure. Specifically, when detecting one kind of complete failure fault that happened within the normal range, the detection performance of ANFIS-based SPE outperforms that of a traditional principal component analysis method. The developed sensor monitoring technique could work well for other kinds of sensor faults resulting from a noxious underground environment.
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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.
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Nitrate control strategies in an activated sludge wastewater treatment process
Wenhao Shen,Erpan Tao,Xiaoquan Chen,Dawei Liu,류홍빈 한국화학공학회 2014 Korean Journal of Chemical Engineering Vol.31 No.3
We studied nitrate control strategies in an activated sludge wastewater treatment process (WWTP) basedon the activated sludge model. Two control strategies, back propagation for proportional-integral-derivative (BP-PID)and adaptive-network based fuzzy inference systems (ANFIS), are applied in the WWTP. The simulation results showthat the simple local constant setpoint control has poor control effects on the nitrate concentration control. However,the ANFIS (4*1) controller, which considers not only the local constant setpoint control of the nitrate concentration,but also three important indices in the effluent--ammonia concentration, total suspended sludge concentration and totalnitrogen concentration--demonstrates good control performance. The results also prove that ANFIS (4*1) controllerhas better control performance than that of the controllers PI, BP-PID and ANFIS (2*1), and that the ANFIS (4*1)controller is effective in improving the effluent quality and maintaining the stability of the effluent quality.
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Modeling of a paper-making wastewater treatment process using a fuzzy neural network
Mingzhi Huang,유창규,Jinquan Wan,Yan Wang,Yongwen Ma,Huiping Zhang,류홍빈,Zhanzhan Hu 한국화학공학회 2012 Korean Journal of Chemical Engineering Vol.29 No.5
An intelligent system that includes a predictive model and a control was developed to predict and control the performance of a wastewater treatment plant. The predictive model was based on fuzzy C-means clustering, fuzzy inference and neural networks. Fuzzy C-means clustering was used to identify model’s architecture, extract and optimize fuzzy rule. When predicting, MAPE was 4.7582% and R was 0.8535. The simulative results indicate that the learning ability and generalization of the model was good, and it can achieve a good predication of effluent COD. The control model was based on a fuzzy neural network model, taking into account the difference between the predicted value of COD and the setpoint. When simulating, R was 0.9164, MAPE was 5.273%, and RMSE was 0.0808, which showed that the FNN control model can effectively change the additive dosages. The control of a paper-making wastewater treatment process in the laboratory using the developed predictive control model and MCGS (monitor and control generated system) software shows the dosage was computed accurately to make the effluent COD remained at the setpoint,when the influent COD value or inflow flowrate was changed. The results indicate that reasonable forecasting and control performances were achieved through the developed system; the maximum error was only 3.67%, and the average relative error was 2%.
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