Ho-Hyun Lee

Intelligent Controller for Constant Control of Residual Chlorine in Water Treatment Process

In this study, chlorine modeling technique based on fuzzy system is proposed to reduce the carcinogenic substance and decide the optimal chlorine injection rate, which is affected by chlorine evaporation rate in sedimentation basin according to detention time, weather and water quality. The additional chlorine meter is installed in the inlet part of sedimentation to reduce the feedback time and implement cascade control, which leads to maintaining the residual chlorine concentration decided by fuzzy rule. It helps to take a preemptive action about long time delay, the characteristics of the disinfection process, and reduce the variation of residual chlorine rate by 7.3 times and the chlorine consumption by 40,000 dollars. It made a significant contribution to supply hygienically safe drinking water.

Application of neuro-fuzzy PID controller for effective post-chlorination in water treatment plant

Abstract The presented chlorine controller system has neglected the travel time for monitoring the amount of chlorine in water treatment plant (WTP). In the present study, an adaptive neuro-fuzzy inference system was used to predict the travel time and chlorine changes that take place at a clear well in a typical WTP. The artificial Neuro-Fuzzy Inference System combined with Proportional Integral Derivative (PID) controller system was applied to optimize the chlorine dosing rate and to minimize the chance of errors. The travel time and the dosing rate were automatically calculated and injected using the proposed model and the controller. The standard deviation of an output chlorine rate was 3.6 and 7 times less than those of an old controller system in real application and in simulation, respectively. It was found that the neuro-fuzzy PID controller made a significant contribution to supply hygienically safe drinking water by considering various conditions including the travel time than the existing methods.

An Ultrasonic Multi-Beam Concentration Meter with a Neuro-Fuzzy Algorithm for Water Treatment Plants

Ultrasonic concentration meters have widely been used at water purification, sewage treatment and waste water treatment plants to sort and transfer high concentration sludges and to control the amount of chemical dosage. When an unusual substance is contained in the sludge, however, the attenuation of ultrasonic waves could be increased or not be transmitted to the receiver. In this case, the value measured by a concentration meter is higher than the actual density value or vibration. As well, it is difficult to automate the residuals treatment process according to the various problems such as sludge attachment or sensor failure. An ultrasonic multi-beam concentration sensor was considered to solve these problems, but an abnormal concentration value of a specific ultrasonic beam degrades the accuracy of the entire measurement in case of using a conventional arithmetic mean for all measurement values, so this paper proposes a method to improve the accuracy of the sludge concentration determination by choosing reliable sensor values and applying a neuro-fuzzy learning algorithm. The newly developed meter is proven to render useful results from a variety of experiments on a real water treatment plant.

Intelligent Controller for Optimal Coagulant Dosage Rate in Water Treatment Process

Chemicals are injected in order to remove a variety of organic substances contained in the water purification plant influent. It can be determined with measuring sedimentation turbidity 4~7 hours later, whether the chemical dosage rate is proper or not, which make the real-time feedback control impossible. In addition, manual operation in accordance with the Jar-Test carried out in the laboratory and the operator`s experience may cause the experimental and human error by the changes of organic characteristics and water quality. Especially at night ad weekend, the rate have been determined only by the operator judgment owing to environment engineer`s absence. Therefore, the decision of optimal chemical dosage rate using proposed intelligent control algorithm is expected to result in real-time injection and cost reduction.

Recursive Neuro-Fuzzy Algorithm for Flow Prediction and Pump On-Off Minimization

In the water treatment process, a main objective is to improve the water quality and also minimize the production costs. To achieve these, an integrated monitoring and control system has been established through flow prediction and pump scheduling. This paper proposes a new integrated solution for predictions and optimal pump control by learning algorithms. Flow prediction has usually been studied for daily or monthly estimation, which is insufficient for real-time control of a water treatment plant (hereafter WTP). An hourly based estimator is proposed to track the steady change of flow demand. Unlike electricity, water can be stored in huge tanks for more than a dozen hours, which can be used for saving energy and increasing water quality. Pump on/off minimization is considered to improve the water quality. If influent water to a water treatment plant varies, then output turbidity and particles are increasing, which could possibly be supplied to citizens. The proposed on/off minimization algorithm is expected to prevent those particles from leaking and to secure public health.