Predicting Iron And Manganese Accumulation Potential In Water Distribution Networks Using Artificial Neural Network

In April 2010 the Water Services Regulation Authority in England and Wales, OFWAT, introduced the Service Incentive Mechanism (SIM) which rates water companies on their performance based on customer satisfaction and either reward or penalise them. In view of this, it has become extremely important for water companies to lower customer complaints due to drinking water discolouration; which is approximately 34% of all customer complaints. Presently, most water companies identify high discolouration risk regions in water distribution networks (WDNs) by selecting areas in the network with high Iron (Fe) and Manganese (Mn) concentrations from their random sampling. With about 315,000 km of water mains in England and Wales, monitoring Fe and Mn concentrations will always be a very difficult and expensive task. In this paper, an artificial neural network (ANN) model was developed to predict Fe and Mn accumulation potential using relevant biological, chemical, hydraulic and pipe-related parameters. The model is able to predict Fe and Mn accumulation potential for each node in a given water supply zone (WSZ). It was observed that regions in the network with high Fe and Mn accumulation potential from the risk maps generated by the model for each of the WSZs also had high customer complaints due to discolouration. This model can be used as a tool to assist in reducing discolouration and customer complaints by helping water resource engineers to identify the high risk regions, investigate the causes of high Fe and Mn accumulation potential in those regions and if possible find solutions to them

Representations and evolutionary operators for the scheduling of pump operations in water distribution networks.

Reducing the energy consumption of water distribution networks has never had more significance. The greatest energy savings can be obtained by carefully scheduling the operations of pumps. Schedules can be defined either implicitly, in terms of other elements of the network such as tank levels, or explicitly by specifying the time during which each pump is on/off. The traditional representation of explicit schedules is a string of binary values with each bit representing pump on/off status during a particular time interval. In this paper, we formally define and analyze two new explicit representations based on time-controlled triggers, where the maximum number of pump switches is established beforehand and the schedule may contain less switches than the maximum. In these representations, a pump schedule is divided into a series of integers with each integer representing the number of hours for which a pump is active/inactive. This reduces the number of potential schedules compared to the binary representation, and allows the algorithm to operate on the feasible region of the search space. We propose evolutionary operators for these two new representations. The new representations and their corresponding operations are compared with the two most-used representations in pump scheduling, namely, binary representation and level-controlled triggers. A detailed statistical analysis of the results indicates which parameters have the greatest effect on the performance of evolutionary algorithms. The empirical results show that an evolutionary algorithm using the proposed representations improves over the results obtained by a recent state-of-the-art Hybrid Genetic Algorithm for pump scheduling using level-controlled triggers

Effect of thermal comfort on occupant productivity in office buildings : response surface analysis

Thermal environment is one of the main factors that influence occupants' comfort and their productivity in office buildings. There is ample research that outlines this relationship between thermal comfort and occupant productivity. However, there is a lack of literature that presents mathematical relationship between them. This paper presents a research experimental study that investigates effects of indoor environmental quality factors on thermal comfort and occupant productivity. This study was conducted by collecting indoor environmental quality parameters using sensors and online survey for twelve months. Data analysis was done using Response Surface Analysis to outline any mathematical relationship between indoor environmental quality and occupant productivity. The outlined relationships confirmed dependencies of occupant thermal comfort and productivity on various indoor environmental factors. These dependencies include the effect of CO2 concentration, VOC concentration. These relationships were analysed to rank nine indoor environmental parameters as per the degree of effect on occupant thermal comfort and productivity. These findings would help design professionals to design better office design that would improve occupants’ comfort and their productivity. Study results have different implications for professionals working in design, construction and operation of office buildings. It is recommended that design guidelines for office buildings should consider occupant productivity and incorporate recommended range for indoor environmental quality parameters in respective categories and criteria

Multi-Objective genetic algorithms for the design of pipe networks.

This paper presents a multiobjective genetic algorithm approach to the design of a water distribution network. The objectives considered are minimization of the network cost and maximization of a reliability measure. In this study, a new reliability measure, called network resilience, is introduced. This measure mimics a designer’s desire of providing excess head above the minimum allowable head at the nodes and of designing reliable loops with practicable pipe diameters. The proposed method produces a set of Pareto-optimal solutions in the search space of cost and network resilience. Genetic algorithms are observed to be poor in handling constraints. To handle constraints in a better way, a constraint handling technique that does not require a penalty coefficient and is applicable to water distribution systems is presented. The present model is applied to two example problems, which are widely reported. Comparison of the present method with other methods revealed that the network resilience based approach gave better results

Multi-Objective genetic algorithms for the design of pipe networks.

This paper presents a multiobjective genetic algorithm approach to the design of a water distribution network. The objectives considered are minimization of the network cost and maximization of a reliability measure. In this study, a new reliability measure, called network resilience, is introduced. This measure mimics a designer’s desire of providing excess head above the minimum allowable head at the nodes and of designing reliable loops with practicable pipe diameters. The proposed method produces a set of Pareto-optimal solutions in the search space of cost and network resilience. Genetic algorithms are observed to be poor in handling constraints. To handle constraints in a better way, a constraint handling technique that does not require a penalty coefficient and is applicable to water distribution systems is presented. The present model is applied to two example problems, which are widely reported. Comparison of the present method with other methods revealed that the network resilience based approach gave better results