폐수처리공정 조업지원시스템 설계 및 구축에 관한 연구

어수영, 정신호, 신동일, 윤인섭

서울대학교 응용화학부

Design and Implementation of An Operations Support System for

Industrial Wastewater Treatment Plants

Sooyoung Eo, Shinho Jeong, Dongil Shin and En Sup Yoon

School of Chemical Engineering, Seoul National University

Introduction

Treatment of wastewater is concerned with the removal of constituents which will deplete oxygen resources of the receiving water to which they are discharged, may stimulate undesirable growth of plants and organisms in the receiving water, or have adverse health or other effects on downstream water uses. Industrial activities, through their manufacturing cycles, generate significant amount of wastewater, which usually does not satisfy environmental regulations so as to be discharged directly to the receiving water streams. The regulations, however, tend to become more strict and restrictive as far as admissible quantities of rejected pollutants are concerned. It is, therefore, of great interest to be able to satisfy the present and future limitations by developing new, reliable means of supervision and treatment of wastewater [1, 2, 3].

Wastewater treatment plants have to be managed very efficiently, particularly when the supervision and fault detection are concerned―any fault implying pollution may cost a lot to the total operation cost of the plant. However, in many wastewater treatment plants, operators can only notice the problems that already have occurred, but hardly prevent them, because they rarely have any computer support that would provide an efficient aid in the analysis of the available information and the decision making. The decision support system developed in this research would improve the operation of the wastewater treatment plant (in the aspect of safety, reliability, and efficiency) and enable a fault-free management of the treatment plant by providing efficient real-time decision aids to operators.

Requirements Analysis and System Design

It was identified that the operators of wastewater treatment plants are generally in need of two types of help during the normal/abnormal operations of the plants: design problem solving and diagnostic problem solving. Due to the fluctuating demands from the market, the upstream process (e.g., especially of manufacturing semiconductors) sends to the treatment plant various input streams of significantly changing flowrates and compositions. In case of solving the design problems, the operators are then supposed to determine the optimal processing conditions for the given input stream to be treated and asked to change the set points accordingly. The other case of problem is diagnosing the causes of suboptimal performances of the process.

One of the main objectives of our development effort is to allow users to rapidly and effectively evaluate the cause(s) of degraded performances at the process that fail to meet effluent quality targets. The designed system achieves this objective by modeling the proven evaluation strategy employed by experts concerned with diagnosing the plant. By observing symptoms and using the diagnostic expertise embedded, it identifies and prioritizes performance limiting factors, such as design, operational, maintenance, and administrative factors, which may be the cause(s) of the suboptimal process performance (see Figure 1).

Key unit models (for example, clarifier, aeration basin, activated sludge, thicker, centrifuge, etc.) comprising the treatment process contain numerical/qualitative models of sufficient details, obtained from first principles and the estimation of process parameters (calibrated using the historical plant data). The knowledge base is composed of the part general to most cases of wastewater treatment processes, the part applicable only to a designated type of process, and the part specific to the actual plant this application is built for. Numerous and wide domains of related knowledge in quantitative and qualitative natures has been collected and analyzed from publicly available sources and by intensive interviews of the process operators and administrators who have wide, long-term experience in dealing with industrial wastewater treatment plants. Fault detection rules were formulated to help provide better explanations and remedial action suggestions for the possible causes of the detected abnormal situations.

The inference is made based on two types of data: measured values by process sensors or laboratory tests and qualitative information observed and inputted by operators. To help the decision making of the operators, multimedia information is also displayed with textual descriptions when the operators' input is important and significantly affects to the final outcome of the diagnosis. Since the dynamic trend information doesn't do a significant role, the diagnostic process shows characteristics similar to the diagnosis of the human body or automobile troubleshooting, i.e., the operators have more time to directly look up and investigate the suspected parts compared to the usual on-line fault detection and diagnosis of chemical plants. So the cooperation of human operators is very important and has big impact to the final result―it is a reason why we implemented this as a decision support system, not as an autonomous diagnostic system.

Figure  An Operations Support System Framework

System Implementation

The validity of the designed framework of the wastewater treatment operations support system was confirmed by building a real application for a wastewater treatment process of a semiconductor manufacturing plant. Effluent discharged from multiple process areas is treated in this plant through a combined physical/chemical and biological wastewater treatment system. The primary capabilities of the implemented system are as follows:

1) It assists operators with maintaining consistent effluent quality compliance as mandated by regulatory agencies.

2) It furnishes a tool to optimize the treatment process and increase operational efficiency.

3) Operators have access to a tool that is easy to use and provides them with quick guidance assisting in the management of the treatment process.

4) It supplies an efficient, customized training platform for the operating personnel using a user-friendly simulator of reliable prediction capability.

A prototype version of the system including the main graphical user interfaces (GUI) was implemented in the G2 real-time expert system shell, and the simulation-related part was interfaced using a customized simulation engine (see Figure 2). The user-friendly interface facilitates the use of the system and allows operators to analyze the results without having extensive knowledge of the program. The implemented system was first validated off-line on the collected real plant data and determined to be useful assisting plant personnel with the continual management and operation of the treatment process. This system is also proven to be very useful for knowledge acquisition and for the training of people concerned with the maintenance or monitoring of the wastewater treatment plant―there are more frequent job shifts in wastewater treatment plants. (Though G2 was very useful and a big help in extracting and revising knowledge base, after the system design has been fixed, the whole system has been re-implemented for wide adoption and distribution in real plant operations using C++ and CLIPS, an expert system shell developed by NASA and publicly available with much less cost than G2.)

Figure  A Screenshot of the Developed Operations Support System (Only the minimal number of windows are simultaneously displayed at the real execution of the program.)

Conclusions

The implemented system is being ported for on-line interfacing and test, and is expected to give a good return for the investment. Since the process-general and specific parts exist as separate modules, applications of this system can be easily built up for various types of wastewater treatment processes. Commercialization of this system as a software package is being planned with more extensive suite of knowledge on the wastewater treatment process.

Acknowledgment

This work was supported in part by LG Corporate Institute of Technology (R&D Center for Environment, Safety & Health) and by Korea Science and Engineering Foundation through the Automation Research Center at Pohang University of Science and Technology.

References

[1] Ketchen, S., Takacs, I. and Lockwood, S., "Model helps operators with process decisions at the GEP-Selkirk Plant", Proceedings of WEFTEC '97, Chicago, Illinois, Vol. 3, 287, October (1997).

[2] Petrides, D., Cruz, R. and Calandranis, J., "Optimization of wastewater treatment facilities using process simulation", Computers chem. Engng, 22S, S339 (1998).

[3] Szafnicki, K. and Graillot, D., "Towards an integrated tool for control, supervision and operator training: Application to industrial wastewater treatment plants", Proceedings of the IFAC On-Line Fault Detection Workshop, Lyon, France, June (1998).