CAPACITY EXPANSION PLANNING MODEL FOR WASTE WATER TREATMENT AND I/I CONTROL1

ABSTRACT: A dynamic programming model is presented for planning the optimal capacity expansion sequence of waste water treatment plants servicing sewerage systems with infiltration/inflow. The model employs a nonlinear deterministic demand function for waste water treatment plant capacity. Model output provides the optimal timing, size, and sequence for treatment plant capacity expansion projects and infiltration/inflow removal projects. An example problem is presented and solved.     

A MODELLING METHOD FOR EVALUATING WATER QUALITY POLICIES IN NONSERIAL RIVER SYSTEMS1

ABSTRACT: This paper describes a modelling method which simplifies the evaluation of water quality policies for nonserial (branching) river systems. The method introduces dummy facilities at the junctions of the major tributary branches with the mainstream as replacements for the facilities and activities on the tributaries. The cost functions for the dummy facilities and the DO and BOD concentrations at the junctions as determined for each tributary are introduced into the mainstream serial system model which is then solved for the optimal values of the mainstream treatment plant efficiencies, the dummy facility effeciencies, and the tributary system DO and BOD concentrations using nonlinear programming. Given the optimum values for the dummy facility efficiencies and the values for the tributary system DO and BOD concentrations, the optimum values for the tributary treatment plant efficiencies are found using nonlinear programming. The method is applied to a river system with a mainstream and one major tributary which contain industrial and municipal organic and thermal polluters and their associated wastewater treatment plants.     

AN APPLICATION OF MATHEMATICAL PROGRAMMING IN PLANNING SURFACE WATER STORAGE1

Designing a surface reservoir involves the concept of reservoir yield. This concept embodies three basic information items: hydrologic regime, active storage volume, and reservoir release policy. In the actual case presented below, the magnitude of the active storage was prescribed by a legal procedure, so that the planning issue became that of determining the reservoir yield given the hydrological information. A stochastic dynamic programming model was formulated to derive a schedule of seasonal optimal reservoir releases and their respective probabilities of occurrence. This schedule is the reservoir yield. The yearly cycle was divided into three seasons representing the actual climatic conditions, and conditional probabilities linking streamflows in consecutive seasons were estimated. An operating policy was postulated, based on the same set of legal decisions that prescribed the active storage volume, and target reservoir releases were assumed. Similarly, target storages at the end of each season were set up. The optimizing/ minimizing criterion in the dynamic programming formulation was the sum of squares of deviations of actual releases and final storage volumes from their respective targets.     

污水处理厂优化设计研究进展

本文对城市污水处理厂优化设计研究发展的历史作了回顾 ,对不同时期的几个系统模型作了简单的介绍 ,并提出了城市污水处理厂优化设计研究中几个有待进一步研究的问题。     

A comparative analysis of methods to represent uncertainty in estimating the cost of constructing wastewater treatment plants

Prediction of construction cost of wastewater treatment facilities could be influential for the economic feasibility of various levels of water pollution control programs. However, construction cost estimation is difficult to precisely evaluate in an uncertain environment and measured quantities are always burdened with different types of cost structures. Therefore, an understanding of the previous development of wastewater treatment plants and of the related construction cost structures of those facilities becomes essential for dealing with an effective regional water pollution control program. But deviations between the observed values and the estimated values are supposed to be due to measurement errors only in the conventional regression models. The inherent uncertainties of the underlying cost structure, where the human estimation is influential, are rarely explored. This paper is designed to recast a well-known problem of construction cost estimation for both domestic and industrial wastewater treatment plants via a comparative framework. Comparisons were made for three technologies of regression analyses, including the conventional least squares regression method, the fuzzy linear regression method, and the newly derived fuzzy goal regression method. The case study, incorporating a complete database with 48 domestic wastewater treatment plants and 29 industrial wastewater treatment plants being collected in Taiwan, implements such a cost estimation procedure in an uncertain environment. Given that the fuzzy structure in regression estimation may account for the inherent human complexity in cost estimation, the fuzzy goal regression method does exhibit more robust results in terms of some criteria. Moderate economy of scale exists in constructing both the domestic and industrial wastewater treatment plants. Findings indicate that the optimal size of a domestic wastewater treatment plant is approximately equivalent to 15,000 m3/day (CMD) and higher in Taiwan. Yet the optimal size of an industrial wastewater treatment plant could fall in between 6000 CMD and 20,000 CMD.     

OPTIMAL CAPITAL INVESTMENT IN THE EXPANSION OF AN EXISTING WATER RESOURCES SYSTEMS1

The study of the optimal expansion of existing water resources systems is of continuing importance because of the rising demand and limited supply of water in many areas of the world, particularly in the southwestern part of the United States of America. This study is concerned with the investigation of the optimal expansion of a realistic water resources system to meet an increasing demand for municipal and industrial use, irrigation, energy, and recreation over a planning horizon of T years. A number of possible dam sites are available for the further regulation of river (canal) flows in the basin and/or the regulation of imported waters into the basin. To maximize, over the set of alternative projects, the sum of discounted present value of net earnings subject to the demands and various institutional, physical and budgetary limits, an optimization problem (Problem I) was formed as a 0-1 mixed integer programming problem and was decomposed into the set of all feasible combinations (Problem II). The economic return was determined for each combination (Problem III). Problem II was solved by a branch and bound procedure which selected each feasible combination of dams while the optimal return for each such combination (Problem III) was found by a network analysis code.     

Optimization of wastewater treatment alternative selection by hierarchy grey relational analysis

This paper describes an innovative systematic approach, namely hierarchy grey relational analysis for optimal selection of wastewater treatment alternatives, based on the application of analytic hierarchy process (AHP) and grey relational analysis (GRA). It can be applied for complicated multicriteria decision-making to obtain scientific and reasonable results. The effectiveness of this approach was verified through a real case study. Four wastewater treatment alternatives (A(2)/O, triple oxidation ditch, anaerobic single oxidation ditch and SBR) were evaluated and compared against multiple economic, technical and administrative performance criteria, including capital cost, operation and maintenance (O and M) cost, land area, removal of nitrogenous and phosphorous pollutants, sludge disposal effect, stability of plant operation, maturity of technology and professional skills required for O and M. The result illustrated that the anaerobic single oxidation ditch was the optimal scheme and would obtain the maximum general benefits for the wastewater treatment plant to be constructed.     

A STOCHASTIC DYNAMIC PROGRAMMING BASED APPROACH TO THE OPERATION OF A MULTI-RESERVOIR SYSTEM1

ABSTRACT: An heuristic iterative technique based upon stochastic dynamic programming is presented for the analysis of the operation of a three reservoir ‘Y’ shaped hydroelectric system. The technique is initiated using historical inflow data for the downstream reservoir. At each iteration the optimal policies for the downstream hydroelectric generating unit are used to provide relative weightings or targets for operation of upstream reservoirs. New input inflows to the downstream reservoir are then obtained by running the historical streamflow record through the optimal policies for the upstream reservoirs. These flows are then used to develop a new operating policy for the downstream reservoir and hence new targets for the upstream reservoirs. The process is continued until the operating policies for each reservoir provide the same overall system benefit for two successive iterations. Results obtained from the procedure are compared to the results obtained by historical operation of the system. The procedure is shown to develop operating policies which give benefits which are as close to the historical benefits as can be expected given the choice of the number of storage state variables.     

Determination of optimum operating conditions for industrial dye wastewater treatment using adaptive heuristic criticism pH control

For a pilot-scale application, pH control in the treatment of highly contaminated dye industrial wastewater containing metallic compounds as the main pollutants has been investigated with a method using adaptive heuristic criticism control (AHCC). Subsequent experimentation on between 12 and 18 l of the wastewater was carried out using statistical experimental design methodology to evaluate the effects of three critical factors: slaked lime (calcium hydroxide, Ca(OH)(2)) concentration, iron chloride (FeCl(3)) concentration and wastewater volume. With these critical factors, the wastewater treatment process is modeled as an appropriate quadratic cost function of the turbidity of the clarified water. The model is optimized with Rosenbrock's method. Response surface topology of the wastewater treatment is given in terms of optimal concentrations of lime water and FeCl(3) and optimal wastewater volume at pH 11.     

Two-stage planning for sustainable water-quality management under uncertainty

In water-quality management problems, uncertainties may exist in a number of impact factors and pollution-related processes (e.g., the volume and strength of industrial wastewater and their variations can be presented as random events through identifying a statistical distribution for each source); moreover, nonlinear relationships may exist among many system components (e.g., cost parameters may be functions of wastewater-discharge levels). In this study, an inexact two-stage stochastic quadratic programming (ITQP) method is developed for water-quality management under uncertainty. It is a hybrid of inexact quadratic programming (IQP) and two-stage stochastic programming (TSP) methods. The developed ITQP can handle not only uncertainties expressed as probability distributions and interval values but also nonlinearities in the objective function. It can be used for analyzing various scenarios that are associated with different levels of economic penalties or opportunity losses caused by improper policies. The ITQP is applied to a case of water-quality management to deal with uncertainties presented in terms of probabilities and intervals and to reflect dynamic interactions between pollutant loading and water quality. Interactive and derivative algorithms are employed for solving the ITQP model. The solutions are presented as combinations of deterministic, interval and distributional information, and can thus facilitate communications for different forms of uncertainties. They are helpful for managers in not only making decisions regarding wastewater discharge but also gaining insight into the tradeoff between the system benefit and the environmental requirement.     

Assessment of AWT systems in the metro Atlanta area

A study was conducted to evaluate the performance of six advanced wastewater treatment facilities using biological nutrient removal processes as the primary mechanism for removing carbon, nitrogen, and phosphorus from domestic wastewater. One year of operating data was obtained from monthly operating reports provided by the Georgia Environmental Protection Division (EPD) in Atlanta. Additional information about facility operations and the types of chemicals used was gathered through review of EPD files and interviews with plant personnel. Data evaluated were: influent and effluent five-day Biochemical Oxygen Demand (BOD(5)); influent and effluent total suspended solids (TSS); influent Total Kjeldahl Nitrogen and effluent Total Nitrogen; and influent and effluent Total Phosphorus (TP). Although varying from plant to plant, effluent requirements for BOD(5), TSS, ammonia, and TP were met. Chemicals utilized, design capacity, and monthly effluent concentrations are presented in this study.     

A multiobjective optimization framework for multicontaminant industrial water network design

The optimal design of multicontaminant industrial water networks according to several objectives is carried out in this paper. The general formulation of the water allocation problem (WAP) is given as a set of nonlinear equations with binary variables representing the presence of interconnections in the network. For optimization purposes, three antagonist objectives are considered: F(1), the freshwater flow-rate at the network entrance, F(2), the water flow-rate at inlet of regeneration units, and F(3), the number of interconnections in the network. The multiobjective problem is solved via a lexicographic strategy, where a mixed-integer nonlinear programming (MINLP) procedure is used at each step. The approach is illustrated by a numerical example taken from the literature involving five processes, one regeneration unit and three contaminants. The set of potential network solutions is provided in the form of a Pareto front. Finally, the strategy for choosing the best network solution among those given by Pareto fronts is presented. This Multiple Criteria Decision Making (MCDM) problem is tackled by means of two approaches: a classical TOPSIS analysis is first implemented and then an innovative strategy based on the global equivalent cost (GEC) in freshwater that turns out to be more efficient for choosing a good network according to a practical point of view.     

REGIONAL PLANNING FOR LAND DISPOSAL OF WASTEWATER1

ABSTRACT This paper describes the development and use of a mixed integer programming model for the planning of land disposal wastewater treatment facilities. Consideration is given to relevant construction and operating costs for land sites, transmission arteries, land acquisition costs, tangible benefits from land use, controls on aquifers, and various other engineering and technical constraints The model is used to determine which land disposal sites should serve which treatment plants, when initial construction should be initiated and completed, and when capacity expansion should occur. The model's application to the St. Louis Standard Metropolitan Statistical Area is illustrated and discussed.     

Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance

A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This process is complex and attains a high degree of nonlinearity due to the presence of bio-organic constituents that are difficult to model using mechanistic approaches. Predicting the plant operational parameters using conventional experimental techniques is also a time consuming step and is an obstacle in the way of efficient control of such processes. In this work, an artificial neural network (ANN) black-box modeling approach was used to acquire the knowledge base of a real wastewater plant and then used as a process model. The study signifies that the ANNs are capable of capturing the plant operation characteristics with a good degree of accuracy. A computer model is developed that incorporates the trained ANN plant model. The developed program is implemented and validated using plant-scale data obtained from a local wastewater treatment plant, namely the Doha West wastewater treatment plant (WWTP). It is used as a valuable performance assessment tool for plant operators and decision makers. The ANN model provided accurate predictions of the effluent stream, in terms of biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solids (TSS) when using COD as an input in the crude supply stream. It can be said that the ANN predictions based on three crude supply inputs together, namely BOD, COD and TSS, resulted in better ANN predictions when using only one crude supply input. Graphical user interface representation of the ANN for the Doha West WWTP data is performed and presented.     

REGIONAL WATER QUALITY MANAGEMENT BY THE GENERALIZED REDUCED GRADIENT METHOD1

ABSTRACT: A river basin-wide water quality management system is considered. The river receives thermal as well as organic wastes. At-source treatment of these pollutants is imposed to control the basin-wide water quality. The related water quality standards are: the minimum DO concentration, the maximum allowable BOD concentration, the maximum allowable stream temperature, and the allowable rise in stream temperature. The general dynamic mathematical model representing water quality in streams and the thermal effects on BOD and DO concentrations is presented. The model is highly nonlinear in nature. The optimal management problem involving the model is solved by a recently developed nonlinear propgramming technique - the generalized reduced gradient (GRG) method. Comparison of results obtained by the GRG method vs. dynamic programming, and of results using a more realistic mathematical model vs. a simple model are presented. The analysis procedure can be applied to designing new and examining existing water quality programs, and to study the influence of alternate policies and constraints.     

A water network optimization using MATLAB—A case study

The methodology of mass and energy integration is widely applied for reduction of water consumption, which belongs to the area of mass integration, and is also energy integration problem if water is considered an energy source (cooling water and steam). This work presents a study of water-consumption reduction through optimization of the process superstructure. A petroleum refinery water network was analyzed and a solution is proposed to minimize the costs of fresh water and wastewater treatment. Three processes which use water, with three contaminants, were monitored and three potential treatment units are proposed. The procedure was based on relaxation of a non-convex non-linear programming problem into a mixed integer linear programming (MILP). The MILP model was further simplified using heuristic rules and solved by using MATLAB.     

珠江三角洲经济区污水系统设计原则

分析了珠江三角洲经济区水环境现状；阐述了城市污水分散处理与集中处理的利弊；指出污水深度处理与回用是形成良性水循环的有效途径，更是经济可持续发展的必然要求；通过对已建排水系统存在问题的分析，探讨了珠江三角洲经济开发区污水系统的设计原则，认为污水系统规划要改变传统设计方法，应方便再生水回用，由经济、技术、地理等综合因素确定排水分区、污水厂厂址与数量，选择适合各排水区域水质水量的工艺流程，预留污水厂与再生水回用管线分期发展的空间，应优先选用国产污水处理设备，尽可能维持城市河涌自然生态。     

Industrial experience of process identification and set-point decision algorithm in a full-scale treatment plant

This paper presents industrial experience of process identification, monitoring, and control in a full-scale wastewater treatment plant. The objectives of this study were (1) to apply and compare different process-identification methods of proportional-integral-derivative (PID) autotuning for stable dissolved oxygen (DO) control, (2) to implement a process monitoring method that estimates the respiration rate simultaneously during the process-identification step, and (3) to propose a simple set-point decision algorithm for determining the appropriate set point of the DO controller for optimal operation of the aeration basin. The proposed method was evaluated in the industrial wastewater treatment facility of an iron- and steel-making plant. Among the process-identification methods, the control signal of the controller's set-point change was best for identifying low-frequency information and enhancing the robustness to low-frequency disturbances. Combined automatic control and set-point decision method reduced the total electricity consumption by 5% and the electricity cost by 15% compared to the fixed gain PID controller, when considering only the surface aerators. Moreover, as a result of improved control performance, the fluctuation of effluent quality decreased and overall effluent water quality was better.     

Environmental and economic feasibility study of a total wastewater treatment network system

The synthesis of distributed wastewater treatment plants (WTPs) has been studied to reduce capital and operating costs associated with wastewater treatment. In this study, the environmental and economic feasibility of a total wastewater treatment network system (TWTNS) including distributed and terminal WTPs was estimated using life cycle assessment (LCA) and life cycle costing (LCC) methods. Wastewater sources and existing distributed and terminal WTPs in an iron and steel plant were networked. The TWTNS was generated from the optimal solution to a mathematical optimization model and compared to a conventional wastewater treatment system (CWTS). The environmental effect scores of the TWTNS were from 29.6% to 68.3% higher than those of the CWTS because of higher electricity consumption required to pump wastewater to the networked WTPs. However, the life cycle cost of the TWTNS was lower than that of the CWTS by 10.1% because of the decrease of the labor cost resulting from the closing of three distributed WTPs. Overall, the TWTNS was no more eco-efficient than the CWTS because the increase of environmental burdens outweighed the decrease of economic costs.     

Leaching of N-nitrosodimethylamine (NDMA) in turfgrass soils during wastewater irrigation

N-nitrosodimethylamine (NDMA) is a carcinogenic by-product of chlorination that is frequently found in municipal wastewater effluent. NDMA is miscible in water and negligibly adsorbed to soil, and therefore may pose a threat to ground water when treated wastewater is used for landscape irrigation. A field study was performed in the summer months under arid Southern California weather conditions to evaluate the leaching potential of NDMA in turfgrass soils during wastewater irrigation. Wastewater was used to irrigate multiple turfgrass plots at 110 to 160% evapotranspiration rate for about 4 mo, and leachate was continuously collected and analyzed for NDMA. The treated wastewater contained relatively high levels of NDMA (114-1820 ng L(-1); mean 930 ng L(-1)). NDMA was detected infrequently in the leachate regardless of the soil type or irrigation schedule. At a method detection limit of 2 ng L(-1), NDMA was only detected in 9 out of 400 leachate samples and when it was detected, the NDMA concentration was less than 5 ng L(-1). NDMA was relatively persistent in the turfgrass soils during laboratory incubation, indicating that mechanisms other than biotransformation, likely volatilization and/or plant uptake, contributed to the rapid dissipation. Under conditions typical of turfgrass irrigation with wastewater effluent it is unlikely that NDMA will contaminate ground water.