An autonomous decision support system for manganese forecasting in subtropical water reservoirs

Manganese monitoring and removal is essential for water utilities in order to avoid supplying discoloured water to consumers. Traditional manganese monitoring in water reservoirs consists of costly and time-consuming manual lake samplings and laboratory analysis. However, vertical profiling systems can automatically collect and remotely transfer a range of physical parameters that affect the manganese cycle. In this study, a manganese prediction model was developed, based on the profiler's historical data and weather forecasts. The model effectively forecasted seven-day ahead manganese concentrations in the epilimnion of Advancetown Lake (Queensland, Australia). The manganese forecasting model was then operationalised into an automatically updated decision support system with a user-friendly graphical interface that is easily accessible and interpretable by water treatment plant operators. The developed tool resulted in a reduction in traditional expensive monitoring while ensuring proactive water treatment management.     

Reclaimed water distribution network design under temporal and spatial growth and demand uncertainties

A significant—but underutilized—water resource is reclaimed water, i.e., treated wastewater that is reintroduced for various purposes. Especially in water scarce regions, reclaimed water is often the only remaining source of water to meet increasing population and water demands. In this paper, we develop a new model formulation for the cost-effective branched reclaimed water network design and solve it with an exact optimization method. We consider both construction and energy costs expended over a twenty-year period. Unlike other formulations, uncertain reclaimed water demands, temporal and spatial population changes are explicitly considered in our two-staged construction and expansion model. In order for the system to meet higher demands during the peak times and to evaluate energy use, we consider two pumping conditions: one with average demands, which is used to compute the average energy consumption, and the other with peak demands, which dominates pipe size and pump station capacity selection. By introducing binary variables that indicate discrete pipe and pump sizes, we linearize the nonlinear hydraulic equations and objective function terms. We develop methods to significantly reduce the problem dimension by exploiting the problem characteristics and network structure. Our computational results indicate that these methods are very effective. Finally, we apply our model to design a reclaimed water network for a realistic municipal system under estimated demand and population scenarios, and analyze the sensitivity of the system to model parameters.     

Application of an expert system using neural network to control the coagulant dosing in water treatment plant

The coagulation process is one of the most important stages in water treatment plant, which involves many complex physical and chemical phenomena. Moreover, coagulant dosing rate is non-linearly correlated to raw water characteristics such as turbidity, conductivity, PH, temperature, etc. As such, coagulation reaction is hard or even impossible to control satisfactorily by conventional methods. Based on neural network and rule models, an expert system for determining the optimum chemical dosage rate is developed and used in a water treatment work, and the results of actual runs show that in the condition of satisfying the demand of drinking water quality, the usage of coagulant is lowered.     

Advanced monitoring platform for industrial wastewater treatment: Multivariable approach using the self-organizing map

Treatment of industrial wastewaters is currently confronting important challenges concerning both cost management of treatment plants and fulfillment of tightening environmental regulations. Online monitoring of wastewater treatment is critical, because changes in the performance of treatment can lead to various problems such as decreased efficiency of purification, decreased energy efficiency, or ineffective use of chemicals. Moreover, changes in the operation of a treatment process can inflict changes that have unforeseen consequences, including an increased amount of harmful effluents, and therefore it is essential for a monitoring system to be able to adapt to various process conditions. It seems, however, that the monitoring systems used currently by the industry are lacking this functionality and are therefore only partially able to meet the needs of modern industry. In addition, there is typically a large amount of measurement data available in the industry, for which advanced data processing and computational tools are needed for monitoring, analysis, and control. For this reason, it would be useful to have a monitoring system which could be able to handle a large amount of measurement data and present the essential information on the state and evolution of the process in a simple, user-friendly and flexible manner. In this paper, we introduce an adaptive multivariable approach based on self-organizing maps (SOM) which can be utilized for advanced monitoring of industrial processes. The system developed can provide a new kind of tool for illustrating the condition and evolution of an industrial wastewater treatment process. The operation of the system is demonstrated using process measurements from an activated sludge treatment plant, which is a part of a pulp and paper plant.     

Discovering rules for water demand prediction: An enhanced rough-set approach

Prediction of consumer demands is a pre-requisite for optimal control of water distribution systems because minimum-cost pumping schedules can be computed if water demands are accurately estimated. This paper presents an enhanced rough-sets method for generating prediction rules from a set of observed data. The proposed method extends upon the standard rough set model by making use of the statistical information inherent in the data to handle incomplete and ambiguous training samples. It also discusses some experimental results from using this method for discovering knowledge on water demand prediction.     

火电厂智慧水务关键技术及信息平台建设研究

针对火电厂耗水量大、能耗物耗高、水复用率低、智能化水平低等关键科学技术难题,基于智慧水务研究成果,提出火电厂智慧水务关键技术及信息平台建设的研究方法。根据火电厂水务信息化面临的现状及形势,确立研究的总体、具体目标。融合现场监测资料和智能分析理论和方法,提出水量平衡智能测算方法,建立火电厂污水处理主要设备的故障诊断及寿命预测智能方法,构建火电厂水处理系统历史和实时成本测算模型。提出了信息平台的网络架构、体系架构和逻辑架构设计方案,信息平台融合火电厂水务管理专家辅助决策系统、火电厂系统运行关键设备及构筑物可靠性分析与评价模型等。为实现集节水型、节能型、环保型、安全型、经济型为一体化的智能火电厂水务管理提供技术支撑。     

Lost in optimisation of water distribution systems? A literature review of system operation

Optimisation of the operation of water distribution systems has been an active research field for almost half a century. It has focused mainly on optimal pump operation to minimise pumping costs and optimal water quality management to ensure that standards at customer nodes are met. This paper provides a systematic review by bringing together over two hundred publications from the past three decades, which are relevant to operational optimisation of water distribution systems, particularly optimal pump operation, valve control and system operation for water quality purposes of both urban drinking and regional multiquality water distribution systems. Uniquely, it also contains substantial and thorough information for over one hundred publications in a tabular form, which lists optimisation models inclusive of objectives, constraints, decision variables, solution methodologies used and other details. Research challenges in terms of simulation models, optimisation model formulation, selection of optimisation method and postprocessing needs have also been identified.     

Computer optimization of refrigeration systems in a textile plant: A case history

A process computer was installed in a large integrated nylon plant in 1976. This dedicated chilled water management system was designed to optimize the operation of chillers and to reduce their energy costs. The computer system was also configured to provide maximum visibility of the operating parameters, machine efficiencies, instrumentation integrity checks, historical data, and energy usage accounting. This paper describes details of the control and optimization strategies, including the overall benefits of the system based on five years of successful operating experience.     

基于改进粒子群优化算法的污水处理过程优化控制

针对前置反硝化工艺污水处理过程,提出了一种基于分工策略粒子群优化算法的优化控制方案.以国际水协会(IWA)开发的基准仿真模型BSM1为基础,综合考虑出水水质、曝气能耗和泵送能耗,通过动态优化底层PI控制器的最佳设定值达到出水水质高、能量消耗少的目的.基于分工策略的粒子群算法具有更强的全局寻优能力,仿真结果表明了该优化方案能够在保证污水水质达标的情况下,有效减少污水处理过程的能耗.     

Cost effective water clarification control using a neurally informed control strategy

The abstraction, treatment and supply of potable (drinking) water presents a range of special problems. Early attempts at introducing and extending the range of automatic control at a water treatment plant were invariably compromised by the poor quality of the instrumentation available. Improved sensor technology and the advent of microprocessor based programmable controllers has allowed the implementation of nonstandard solutions. This paper describes the steps followed in developing an accurate feedforward automatic clarification control strategy. The existing strategy was occasionally compromised when a colour measurement was corrupted, usually under conditions of high turbidity. It should be emphasised that the colour monitor used is being asked to operate at and beyond the range of its original design specification. This occurs because there are no other alternative instruments. The paper shows how the use of an artificial neural network produces a solution to a seemingly intractable problem.     

Hybrid-biomarker case-based reasoning system for water pollution assessment in Abou Hammad Sharkia,Egypt

Water pollution by organic materials or metals is one of the problems that threaten humanity, both nowadays and over the next decades. Morphological changes in Nile Tilapia “Oreochromis niloticus” fish liver and gills can also represent the adaptation strategies to maintain some physiological functions or to assess acute and chronic exposure to chemicals found in water and sediments. This paper presents an automatic system for assessing water quality, in Sharkia Governorate – Egypt, based on microscopic images of fish gills and liver. The proposed system used fish gills and liver as hybrid-biomarker in order to detect water pollution. It utilized case-based reasoning (CBR) for indicating the degree of water quality based on the different histopathological changes in fish gills and liver microscopic images. Various performance evaluation metrics namely, retrieval accuracy, receiver operating characteristic (ROC) curves, F-measure, and G-mean have been used in order to objectively indicate the true performance of the system considering the unbalanced data. Experimental results showed that the proposed hybrid-biomarker CBR based system achieved water quality prediction accuracy of 97.9% using cosine distance similarity measure. Also, it outperformed both SVMs and LDA classifiers for the tested microscopic images dataset.     

洱海2005～2014年水质状况及变化分析

以洱海湖区2005—2014年的水质监测资料为基础,运用内梅罗水污染指数法进行分析计算,结合湖泊富营养化状态,对洱海水质及这一段时间的变化趋势进行分析评价。评价结果表明,洱海水质处于清洁状态,保持在中营养状态,湖泊富营养化进程减缓,磷作为内梅罗指数最大贡献因子,成为影响洱海水质的主要污染物。洱海枯水期水质好于丰水期水质,污染以面源污染为主,洱海水环境保护工作取得成效。     

An automatic energy saving strategy for a water dispenser based on user behavior

Nowadays, numerous public buildings provide water dispensers to supply drinking water which causes more energy consumption. A typical water dispenser periodically heats and cools the water to ensure that hot, warm, and cold water are always available for the user. However, this mechanism is inefficient because the users do not request hot and cool water continuously. Ideally, the boiling and cooling schedule should follow the demand pattern to save electricity consumption. When no demand, a water dispenser can enter a sleep mode.Therefore, this study presents an automatic energy-saving strategy for a water dispenser based on user behavior. The proposed system allows the water dispenser to automatically determine the appropriate time to heat, boil, and enter sleep mode based on user behavior. The proposed control strategy involves several steps. First, it collects historical data, analyzes water consumption behavior. The sensors installed in the water dispenser collect water consumption data. Second, this study applies Recurrent Neural Networks with Long-Short Term Memory to predict future water consumption. Finally, the proposed system utilizes the prediction result to determine heating, cooling, and sleep mode schedule.This study uses a water dispenser on a university campus as a prototype to test the proposed system. The effectiveness of the proposed system is measured by two factors, namely electricity consumption, and customer satisfaction. These two parameters are chosen because the proposed system should reduce electricity consumption while maintaining hot and cold water availability whenever needed. According to the simulation results, the proposed controlling strategy can reduce electricity consumption up to 28% monthly while maintaining a service level of 97%. This result shows that the proposed system is a good control system for water dispensers. By applying this controlling system, public buildings could reduce their energy bills without sacrificing their provision of drinking water.     

基于单片机水塔的水位控制系统仿真模型研究

为了达到节能的目的,提高供水系统的质量,考虑采用单片机技术,设计出一套实用水位自动控制方案,文章阐述了自动化装置在水塔水位控制系统中的应用,方案在硬件基础上配合软件实现了高、低警戒水位报警、人工给水的工作方式,完成水塔水位控制系统的设计。     

A software platform for process monitoring: Applications to water treatment

Effective online monitoring of the performance of water treatment is critical. Industrial wastewater treatment, for example, has to confront important challenges concerning both cost management of treatment plants and fulfillment of regulations. Moreover, when drinking water is produced, water safety is an important consideration, and possible changes in water quality should be detected as soon as possible. On the other hand, the large amount of measurement data available creates its own challenges for water treatment processes, which means that advanced tools for monitoring, analysis, and control are often required. Therefore, it would be useful to have a monitoring system which is able to handle all available measurements and present the available information in a simple, user-friendly and flexible manner. In this paper, we introduce a piece of software which can be utilized in the monitoring of water treatment processes. The structure of the software is designed so that it can be easily modified according to the user’s needs. The system is demonstrated using process measurements from an activated sludge treatment plant, which is part of a pulp and paper plant, and from a pilot treatment plant for producing drinking water.     

A Bayesian method for multi-pollution source water quality model and seasonal water quality management in river segments

Excessive pollutant discharge from multi-pollution resources can lead to a rise in downriver contaminant concentration in river segments. A multi-pollution source water quality model (MPSWQM) was integrated with Bayesian statistics to develop a robust method for supporting load (I) reduction and effective water quality management in the Harbin City Reach of the Songhua River system in northeastern China. The monthly water quality data observed during the period 2005–2010 was analyzed and compared, using ammonia as the study variable. The decay rate (k) was considered a key factor in the MPSWQM, and the distribution curve of k was estimated for the whole year. The distribution curves indicated small differences between the marginal distribution of k of each period and that water quality management strategies can be designed seasonally. From the curves, decision makers could pick up key posterior values of k in each month to attain the water quality goal at any specified time. Such flexibility is an effective way to improve the robustness of water quality management. For understanding the potential collinearity of k and I, a sensitivity test of k for I_2i (loadings in segment 2 of the study river) was done under certain water quality goals. It indicated that the posterior distributions of I_2i show seasonal variation and are sensitive to the marginal posteriors of k. Thus, the seasonal posteriors of k were selected according to the marginal distributions and used to estimate I_2i in next water quality management. All kinds of pollutant sources, including polluted branches, point and non-point source, can be identified for multiple scenarios. The analysis enables decision makers to assess the influence of each loading and how best to manage water quality targets in each period. Decision makers can also visualize potential load reductions under different water quality goals. The results show that the proposed method is robust for management of multi-pollutant loadings under different water quality goals to help ensure that the water quality of river segments meets targeted goals.     

过程工业水分配网络系统集成的NLP模型

针对单组分杂质用水和废水处理网络同步集成最优化问题,以最小总操作费用为目标,建立了非线性规划(NLP)模型。该模型既考虑了新鲜水费用和水处理费用之间存在的耦合关系以实现二者的优化均衡,又考虑了环境排放标准及分散式处理网络对水网络集成的影响。采用商业优化软件GAMs对算例问题进行求解,结果表明本文提出的模型能有效处理工业水分配网络系统的优化集成问题,与用水网络和废水处理网络分别集成相比,在年总费用上有不同程度的减小,较好地实现了过程工业在满足环保排放规定前提下的经济效益最大化。     

Automatic generation of water distribution systems based on GIS data

In the field of water distribution system (WDS) analysis, case study research is needed for testing or benchmarking optimisation strategies and newly developed software. However, data availability for the investigation of real cases is limited due to time and cost needed for data collection and model setup. We present a new algorithm that addresses this problem by generating WDSs from GIS using population density, housing density and elevation as input data. We show that the resulting WDSs are comparable to actual systems in terms of network properties and hydraulic performance. For example, comparing the pressure heads for an actual and a generated WDS results in pressure head differences of ±4 m or less for 75% of the supply area. Although elements like valves and pumps are not included, the new methodology can provide water distribution systems of varying levels of complexity (e.g., network layouts, connectivity, etc.) to allow testing design/optimisation algorithms on a large number of networks. The new approach can be used to estimate the construction costs of planned WDSs aimed at addressing population growth or at comparisons of different expansion strategies in growth corridors.     

Modelling of a dual circuit induced draft cooling water system for control and optimisation purposes

The successful operation of any petrochemical plant is dependent on the use of several utilities which may include electricity, steam, compressed air, cooling media, refrigeration media, nitrogen, condensate and fuel gas. These utilities form a significant portion of the fixed cost associated with running a plant. Utility optimisation has not received much attention until recently, driven by rising energy costs, stricter environmental policies, more competitive markets, and the threat of climate change. The generation, preparation, and transportation of utilities require energy and therefore should be optimised to reduce losses and improve operating efficiency. One example of such a utility is a cooling water system. This paper describes the modelling of a dual circuit induced draft cooling water system for control and optimisation purposes. The derived model is verified with plant data indicating promising results. The model is represented in a steady-state algebraic form as well as a dynamic state-space form. This provides a convenient basis for simulation studies and controller/optimiser design.