Summary on "Development of a computerized decision support system controls the aeration tank»

  1. Purpose and objectives of the study
  2. Mathematical model of the problem
  3. Fuzzy Logic
  4. Conclusion
  5. USEDLIST OF USED LITERATURE

Purpose and objectives of the study

     The aim of this work is to improve the methods and algorithms for processing information to support management decision-making wastewater treatment based on the theory of fuzzy sets.      In the present study were as follows:
   – study of the parameters of the object of computerization;
   – Building an object-event model of the object;
   – development and implementation of decision support systems.

Mathematical model of the problem

     The process of biological treatment is shown in Figure 1.

Figure 1 – General view of the process of biological wastewater treatment

     Where 1 – Not Purified Water, 2 – Aerator, 3 – secondary clarifier, 4 – Purified water, 5 – Air 6 – Compressor 7 – Exhaust sucks, 8 – pump, 9 – Activated sludge
     The most important factors influencing the development and viability of activated sludge, as well as the quality of biological treatment are: temperature, nutrient availability, content of dissolved oxygen in the sludge mixture, pH, presence of toxins [6 ]. Satisfactory performance in the aeration tanks is largely determined by technological and operational mode where the primary importance are:
   – The optimal ratio between the concentration of pollutants present in wastewater, and the working dose of activated sludge by weight;
   – The necessary time of contact of contaminated wastewater from activated sludge;
   – Sufficient aerobic system.
     Smaller doses of an effect of increasing silt loads and reducing the quality of treatment with increasing doses of impediment to the effective separation of sludge and treated water in the secondary sedimentation tanks. Violation of the optimum ratio between the concentration of pollutants in the water and working dose of sludge leads to a deterioration in its sedimentation properties, and as a consequence, an increase of sludge index. One of the main requirements for sludge index - the stability of its value, which indicates a satisfactory living conditions of the sludge and satisfactory mode of operation of structures (the optimal amount of silt removed from the system and maintained a normal dose of the return sludge) [6]. Poor aeration conditions for activated sludge may be due to the following reasons:
   – Deposits and mikrozalezhami poorly stirred sludge in different parts of aerated zone;
   – Increasing specific loads on the activated sludge due to the increase of dissolved organic matter entering the clean waters;
   – The effects of toxicants on activated sludge;
   – Increase kislorodpogloschaemosti sludge due to violations of the discharge of sludge from secondary clarifiers;
   – Exceeded the optimal concentration of return sludge (lack of oxygen occurs with increasing biomass of activated sludge).
     Performed biochemical analysis of the process of water purification to determine the following disturbing factors:
   – The temperature of wastewater, τ SW ;
   – Concentration of pollutants in wastewater, s i ;
   – The concentration of toxic substances, s T .
     The main technological parameters (controlled variables) that characterize the efficiency of biological treatment are:
   – Mass load on activated sludge, L S ;
   – Concentration of contaminants at the outlet aerator, s;
   – The rate of oxygen consumption by activated sludge, dQ sour / dt;
   – Concentration of microorganisms in the aerator, with c x .
     Main control, which allows to influence the controlled variables are:
   – Consumption of air supplied to the aeration tanks, Q air ;
   – Consumption of activated sludge fed to the aeration tanks, Q IL .
     Analysis of biochemical treatment allows you to decompose and identify the following key process modules: aerator, compressor station, pump installation. Consider the basic relations describing the physical processes in each process module. The most complex technological module is the aerator. Mass balance equation takes the form of micro-organisms:
V * (dc x dt) = Q IL * a xr - (Q + Q IL ) * c x + V * (μ * c x - b * c x ) (1)
     Where V - volume of sewage; c x - the concentration of microorganisms in the aerators; c xr - the concentration of microorganisms in the return sludge; Q - input stream (flow) wastewater; Q IL - stream (flow) return sludge; b - specific index of the death of microorganisms; μ - specific growth rate of microorganisms depends on the concentration of contaminants:
μ = μ ^ * s (K + s) (2)

     Where μ ^ - the maximum rate of growth of microorganisms; K - a constant parameter that depends on the design of the aerator; s - concentration of contaminants in the aerators.
     Balance equation for contaminants in aerators can be written as:
V * ds dt = Q * s i - Q IL * s - (Q + Q IL ) * s - V * μ Y * c x (3)

     Where s i - the concentration of pollutants in the inflow of sewage; Y - reproduction rate.
     Mass balance equation for dissolved oxygen in the aeration tank can be written as:
(dc sour ) dt = α * Q air (with S sour - a sour ) - (dQ sour ) / dt (4)

     Where c sour - the concentration of dissolved oxygen; α - coefficient of solubility of oxygen with S sour - equilibrium concentration of oxygen in water (saturation concentration).
     The load on the sludge - a ratio of cast dirt and mud mass per unit time. As a measure of the mass of mud take 1 g of dry matter of sludge. As a measure of pollution are taking their quantitative equivalents - biochemical oxygen demand (BOD), chemical oxygen demand (COD). The load on the sludge is the main controlled quantity, with an impact both on other controlled settings, and adjustable sizes. This means that when the load on the sludge will be different: the ratio of maximum rate of oxygen transfer to the rate of consumption of the cells, the specific increase of silt per unit value of BOD, the relative increase of silt on its number in the system and other dependencies.
     The load on the sludge is estimated as the total amount of organic pollution entering the building, referred to the total dry weight of ashless of silt.
L S = (s i * (Q + Q IL )) (s * (1 - Z) * V) (5)

     Where Z - the ash content of sludge.
     Provide silt mixture of oxygen must match the rate of its consumption. In turn, the concentration of activated sludge in the aeration tank necessitates the flow rate of oxygen in the aeration tank. Oxygen consumption rate indicates the degree of activity of activated sludge and its degree of regeneration.

Fuzzy Logic

     Fuzzy system (NS) - a system, a feature whose description is:
   – Fuzzy specification of parameters;
   – Unclear description of input and output variables of the system;
   – Unclear description of the system on the basis of production "if ... then ..." rules.
     Process control system is directly connected with the output variable fuzzy control systems, but the result of fuzzy inference is fuzzy, but the physical execution unit is not able to accept such a command. Need special mathematical methods to move from fuzzy values ​​of variables to a fully defined. In general, the whole process of fuzzy control can be divided into several stages: fuzzification, the development of fuzzy rules and defuzzification.
     Fuzzification (go to the vagueness). this stage, the exact values of input variables are converted to values of linguistic variables through the application of certain provisions of the fuzzy sets theory - namely, with certain membership functions. [8]
     Linguistic variables. In fuzzy logic, the values of any size do not appear to numbers, and words of natural language and are called "terms." [8]
     Membership functions. Affiliation of each exact value to one of the terms of the linguistic variable is defined by membership functions.
     Development of fuzzy rules. At this stage, determined by production rules that link the linguistic variables. Most of fuzzy systems use production rules to describe the relationships between linguistic variables. Typical production rules consist of antecedent (IF part ...) and consequent (THEN part ...). Antecedent may contain more than one parcel. In this case, they are combined by logical connectives "AND" or "OR».
     Defuzzification (elimination of vagueness). At this stage the transition from the fuzzy values of the variables to determine the physical parameters that can serve as the executive teams of the device. [8]

Conclusion

     Existing automation tools designed for water purification, aimed mainly at collecting and storing information. The conclusion about the need to automate the process of decision-making based on artificial intelligence. Shows that the existing decision support system for wastewater treatment can not take into account all the features of the purification process and the need to combine them with the knowledge and experience of experts in this field.

USEDLIST OF USED LITERATURE

  1. Baeza, J., E. Ferreira, and J. Lafuente. Knowledge-based supervision and control of wastewater treatment plant: a real-time implementation. //Water Science & Technology, 2000, 41(12).
  2. Riano, D. Learning rules within a framework of environmental sciences.//In ECAI 98 - W7 (BESAI98) workshop notes, Brighton, UK, 1998
  3. Yang, C.T. and Kao, J.J. An expert-system for selecting and sequencing wastewater treatment processes. //Water Science & Technology, 34(3-4), 1996
  4. Osmond D. L., Gannon R. W., etc. WATERSHEDSS // AWRA Journal of the American Water Resources Association, Volume 33, No. 2, April 1997.
  5. Жмур Н.С. Технологические и биохимические процессы очистки сточных вод на сооружениях с аэротенками. М., АКВАРОС, 2003. – 512 с.
  6. Алиев Р.А. Управление производством при нечеткой исходной информации/ Церковный А. Э., Мамедова Г.А. М: Энергоатомиздат, 1991. – 240 с.
  7. Федюн Р.В. , Попов В.А., Найденова Т.В. Принципы построения динамической модели процесса биохимической водоочистки.
  8. Пивкин В.Я., Бакулин Е.П., Кореньков Д.И. Нечеткие множества в системах управления.
  9. НПЦ Водоочистка [электронный ресурс]. - http://prom-water.ru/base/analizsposobov/
  10. SlideFinder [электронный ресурс]. - http://www.slidefinder.net/
IMPORTANT REMARK

During the writing this abstract the master's work wasn’t finished yet. The final ending is planning on December, 2009. The complete text of work and all the information about work you can receive from the author or him scientific adviser after the mentioned date.

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