• Korean Chemical Engineering Research
• /
• v.52 no.5
• /
• pp.581-586
• /
• 2014

Main objects for wastewater treatment operation are to maintain effluent water quality and minimize operation cost. However, the optimal operation is difficult because of the change of influent flow rate and concentrations, the nonlinear dynamics of microbiology growth rate and other environmental factors. Therefore, many wastewater treatment plants are operated for much more redundant oxygen or chemical dosing than the necessary. In this study, the optimal control scheme for dissolved oxygen (DO) is suggested to prevent over-aeration and the reduction of the electric cost in plant operation while maintaining the dissolved oxygen (DO) concentration for the metabolism of microorganisms in oxic reactor. The oxygen uptake rate (OUR) is real-time measured for the identification of influent characterization and the identification of microorganisms' oxygen requirement in oxic reactor. Optimal DO set-point needed for the micro-organism is suggested based on real-time measurement of oxygen uptake of micro-organism and the control of air blower. Therefore, both stable effluent quality and minimization of electric cost are satisfied with a suggested optimal set-point decision system by providing the necessary oxygen supply requirement to the micro-organisms coping with the variations of influent loading.

• Journal of Energy Engineering
• /
• v.23 no.2
• /
• pp.49-56
• /
• 2014

It is important to keep stable effluent water quality and minimize operation cost in biological wastewater treatment plant. However, the optimal operation is difficult because of the change of influent flow rate and concentrations, the nonlinear dynamics of microbiology growth rate and other environmental factors. Therefore, many wastewater treatment plants are operated for much more redundant oxygen or chemical dosing than the necessary. In this study, the optimal control scheme for dissolved oxygen (DO) is suggested to prevent over-aeration and the reduction of the electric cost in plant operation while maintaining the dissolved oxygen (DO) concentration for the metabolism of microorganisms in oxic reactor. For optimal control, The oxygen uptake rate (OUR) is realtime measured for the identification of influent characterization and the identification of microorganisms' oxygen requirement in oxic reactor. Optimal DO seT-Point needed for the microorganism is suggested based on real time measurement of oxygen uptake of microorganism and the control of air blower. Therefore, both stable effluent quality and minimization of electric cost are satisfied with a suggested optimal setpoint decision system by providing the necessary oxygen supply requirement to the microorganisms coping with the variations of influent loading.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.44 no.12
• /
• pp.1043-1053
• /
• 2016

In this paper, the application of Parallel Distributed Compensation (PDC) controller for fixed pitch rotary wing system was studied. For nonlinear modeling, T-S fuzzy model was utilized to advance system control including the tilt type UAV. PDC controller was designed through the Linear Matrix Inequality (LMI). Experiments for determining the applicability and feasibility of PDC were performed using the 1 axis attitude control equipment and simulation. To verify the performance and characteristics of the controller, Mathworks Co. Simulink was used. After then, the PDC controller performance was verified and the results with developed controller using a 1 axis attitude control equipment were compared. Verification of the feasibility of PDC controller for the fixed pitch rotary wing system and identification of the overall performance and improvement analysis was conducted based on the experimental results.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.10 no.5
• /
• pp.487-496
• /
• 2000

In this paper, the Multi-FNN(Fuzzy-Neural Networks) model is identified and optimized using HCM(Hard C-Means) clustering method and genetic algorithms. The proposed Multi-FNN is based on Yamakawa's FNN and uses simplified inference as fuzzy inference method and error back propagation algorithm as learning rules. We use a HCM clustering and Genetic Algorithms(GAs) to identify both the structure and the parameters of a Multi-FNN model. Here, HCM clustering method, which is carried out for the process data preprocessing of system modeling, is utilized to determine the structure of Multi-FNN according to the divisions of input-output space using I/O process data. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. A aggregate performance index with a weighting factor is used to achieve a sound balance between approximation and generalization abilities of the model. The aggregate performance index stands for an aggregate objective function with a weighting factor to consider a mutual balance and dependency between approximation and predictive abilities. According to the selection and adjustment of a weighting factor of this aggregate abjective function which depends on the number of data and a certain degree of nonlinearity, we show that it is available and effective to design an optimal Multi-FNN model. To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.