Scheduling Of Pumping Operations Research Articles

A solution framework for the long-term scheduling and inventory management of straight pipeline systems with multiple-sources

This paper addresses the long-term scheduling of pumping and delivery operations on straight multiproduct pipeline systems connecting multiple-sources to multiple-destinations. A generic solution framework is proposed, combining heuristic procedures and mixed integer linear programming (MILP) models. The pipeline scheduling problem is decomposed into two sub-problems: the allocation and sequencing of pumping operations of the initial source, and the scheduling of delivery operations and also pumping operations of the intermediate sources. An innovative execution procedure using a rolling horizon strategy is developed to solve both problems. We focus on a new discrete-time MILP model formulation, covering several operational aspects of real-world pipeline systems, such as rigorous treatment of forbidden sequences and preventive tank maintenance and pipeline maintenance periods, while considering rigorous inventory management during the scheduling horizon. The approach is validated by solving an illustrative example and two real-world based pipeline problems. Results show that the proposed framework is able to obtain solutions in reasonable run time with detailed inventory management and respecting several operational constraints of the system.

Scheduling of a single-source multiproduct pipeline system by a matheuristic approach: Combining simulated annealing and MILP

Pipeline systems are used for distribution of oil derivatives, where the optimization of transport activities impact considerably in the oil company’s economy. We focused on the scheduling of pumping and delivery operations on straight multiproduct pipeline systems connecting a single-source to multiple-destinations. This paper develops new preprocessing heuristics and a discrete-time mixed integer linear programming (MILP) formulation for the allocation and sequencing activity (ASM model). The new MILP model includes constraints concerning the scheduling of pumping interruptions considering stoppages in order to avoid inventory problems. A preprocessing block is also proposed to better estimate the delivery operations for the initialization batches using a simulated annealing metaheuristic to provide a more robust input data for the ASM model. The results for two case studies of a real-world pipeline system were studied. Better inventory management is observed when facing low demand instances due to its capacity to effectively propose pipeline interruptions.

Exploitation of pump-and-treat remediation systems for characterization of hydraulic heterogeneity

Pump-and-treat (P&T) is a widely adopted technology for groundwater containment/remediation. Over the past decades, we have recognized that mapping aquifer heterogeneity, especially the low hydraulic conductivity (K) zones, has become an urgent need in remediation efforts. Meanwhile, many studies have proved that hydraulic tomography (HT) utilizing groundwater responses from a series of cross-hole pumping tests is a viable technology for detailing hydraulic heterogeneity (high and low K zones) of aquifers. At a P&T field site, operation of pumping and injection wells often vary, creating changes in flow fields analogous to those in HT surveys. Therefore, collecting groundwater monitoring data and analyzing them is tantamount to a cost-effective large-scale HT survey during P&T operations. To demonstrate this idea, water level data was collected at 41 wells for one year at the U.S. Air Force Plant 44 (AFP44) P&T site in Tucson, Arizona, where the pumping and injection rates of the P&T system were changed many times during the year. This dataset was analyzed with a geostatistical inverse model to estimate the hydraulic property distribution, which can reasonably predict the pressure responses of the site under different conditions. Moreover, we showed that large-scale geological information, built based on 245 borehole logs at the site, could improve the model calibration performance through a two-step calibration procedure. Additionally, we used synthetic experiments to verify that the use of a larger variety of P&T configurations can yield non-redundant HT information, and enhance site characterization. Overall, we demonstrated that the proper scheduling of pumping and injection operations at a P&T site and collecting corresponding groundwater responses are equivalent to conducting a large-scale HT survey, with minimal additional costs.

A matheuristic decomposition approach for the scheduling of a single-source and multiple destinations pipeline system

An improvement on the scheduling of pumping and delivery operations in an installed pipeline network can lead to considerable profits to the using companies, such as oil companies. This paper proposes a decomposition approach that integrates heuristic procedures and mixed integer linear programming (MILP) models, a matheuristic, to solve the long-term scheduling of a pipeline system, which connects a single-source to multiple distribution centers. The approach provides a rigorous inventory management and flow rate control taking into account several operational aspects, such as simultaneous deliveries, and prespecified periods of tank maintenance and pipeline maintenance. To validate the developed approach, two case studies were devised. In case study 1, several instances of an illustrative network were solved and case study 2 addressed three examples of a real-world network: base instance; extended instance with maintenance periods; and model performance tests. Valid solutions that can be operationally implemented were obtained for all executions in a reasonable computational time. Detailed discussions of the obtained solutions are presented and indicate an inventory control in accordance with operational requirements.

Optimizing Pump Operations in Water Supply Networks Through Genetic Algorithms

Proper scheduling of pump operations (i.e., the specific combinations of pumps and operating parameters to meet system requirements) can yield substantial savings in energy costs for water supply systems. In recent decades, optimization procedures have been developed to solve this complex and multiobjective problem. In the current research, a genetic algorithm method was selected to optimize pump operations to reduce energy consumption while maintaining good service. The method was then applied to the water supply network of Milan, Italy, a large, complex system in which hydraulic head is maintained by the action of pumping stations. On the basis of real data and following field testing, the operations of the network and its pumping stations were first simulated with specially developed software that uses the EPANET Toolkit and then optimized with the proposed method. When checked against real data, simulation results indicated that application of the optimization procedures could produce significant improvement and cost savings.

An Application of the Harmony-Search Multi-Objective (HSMO) Optimization Algorithm for the Solution of Pump Scheduling Problem

In hydraulic systems, water is often pumped to reach higher elevations, so as to ensure the minimum required pressure and guarantee adequate service level. However, pumps cannot be instantly activated and people do not consume the resource in uniform mode throughout the day. To avoid direct pumping, water can be stored in tanks at a higher elevation, so that it can be supplied whenever there is a higher demand. Because of the significant costs required for pumping, energy-saving in water supply systems is one of the most challenging issues to ensure optimal management of water systems. Careful scheduling of pumping operations may lead not only to energy savings, but alsoto prevent damages, as consequence of reduction of operation times and switches. By means of computer simulation, an optimal schedule of pumps can be achieved using optimization algorithms. In this paper, a harmony-search multi-objective (HSMO) optimization approach is adapted to the pump scheduling problem. The model interfaces with the popular hydraulic solver, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the selected schedules. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to a case study, showing that the results are comparable with those of competitive meta-heuristic algorithms (e.g. Genetic Algorithms) and pointing out the suitability of the HSMO algorithm for pumping optimization.

Optimal operation efficiency and control of water pumps in multiple water reservoir system: case study in Korea

Pumping regimes for water distribution systems have been established to reduce pump operation costs. Optimization of water reservoir networks has been studied extensively for operating pumps efficiently. According to research, optimization reduces pump operation costs by approximately 10–20%. However, comparing economic benefits per country would be too complex because the effects of optimization differ depending on the power cost estimation system of each country and the existing operations. This study presents an optimization algorithm to minimize the operating costs of an entire system via the application of dynamic programming. Case studies were conducted to apply the algorithm to real values in four scenarios. By setting cost minimization as an objective, an optimum water pump operation plan was derived that encompasses operating conditions, variations in water demand, and energy costs. Results showed that operating a standby pump in an existing system can increase the system's efficiency. In addition, even if hourly water consumption remains constant, each case requires its own operating method, because each case has different operational costs. By considering various conditions such as those mentioned in this study, future pump operation schedules can be optimized and stabilized to maintain economic water supply.

A Pump Operation Scheduling Algorithm for Large-Scale Water Supply Systems

The operation of integrated water supply systems has become increasingly complicated owing to reorganization of waterworks. We developed a new method to plan pump operation schedules for large-scale water supply systems. In the proposed method, we define solution space as a network model in which the nodes are cumulative flow rates and the edges are the flow rates at each time step. We treat pump scheduling as a shortest path problem, and the A* search algorithm is used to find the optimum path. One of the characteristics of the proposed method is that the search area is restricted by constraints, such as the water level of tanks. The experimental results show that calculation times increase quadratically, nevertheless, the number of solution states shows an exponential growth with increasing number of pumps. We confirmed that the proposed method can be used effectively for large-scale water supply systems.

Solving optimum operation of single pump unit problem with ant colony optimization (ACO) algorithm

For pumping stations, the effective scheduling of daily pump operations from solutions to the optimum design operation problem is one of the greatest potential areas for energy cost-savings, there are some difficulties in solving this problem with traditional optimization methods due to the multimodality of the solution region. In this case, an ACO model for optimum operation of pumping unit is proposed and the solution method by ants searching is presented by rationally setting the object function and constrained conditions. A weighted directed graph was constructed and feasible solutions may be found by iteratively searching of artificial ants, and then the optimal solution can be obtained by applying the rule of state transition and the pheromone updating. An example calculation was conducted and the minimum cost was found as 4.9979. The result of ant colony algorithm was compared with the result from dynamic programming or evolutionary solving method in commercial software under the same discrete condition. The result of ACO is better and the computing time is shorter which indicates that ACO algorithm can provide a high application value to the field of optimal operation of pumping stations and related fields.

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 fewer than the maximum number of switches. 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 is an improvement over the results obtained by a recent state of the art hybrid genetic algorithm for pump scheduling using level-controlled triggers.

Risk classification and uncertainty propagation for virtual water distribution systems

While the secrecy of real water distribution system data is crucial, it poses difficulty for research as results cannot be publicized. This data includes topological layouts of pipe networks, pump operation schedules, and water demands. Therefore, a library of virtual water distribution systems can be an important research tool for comparative development of analytical methods. A virtual city, “Micropolis”, has been developed, including a comprehensive water distribution system, as a first entry into such a library. This virtual city of 5000 residents is fully described in both geographic information systems (GIS) and EPANet hydraulic model frameworks. A risk classification scheme and Monte Carlo analysis are employed for an attempted water supply contamination attack. Model inputs to be considered include uncertainties in: daily water demand, seasonal demand, initial storage tank levels, the time of day a contamination event is initiated, duration of contamination event, and contaminant quantity. Findings show that reasonable uncertainties in model inputs produce high variability in exposure levels. It is also shown that exposure level distributions experience noticeable sensitivities to population clusters within the contaminant spread area. High uncertainties in exposure patterns lead to greater resources needed for more effective mitigation strategies.

Optimal design and operation of irrigation pumping stations using mathematical programming and Genetic Algorithm (GA)

For many water authorities worldwide, one of the greatest potential areas for energy savings is in pump selection and in the related effective scheduling of daily pump operations. The optimal control and operation of an irrigation pumping station is achieved here by first solving the nonlinear governing model using Lagrange Multipliers (LM) and then through Genetic Algorithm (GA) approach. Computation in both methods is driven by an objective function that includes operating and capital costs subject to various performance and hydraulic constraints. The LM approach first specifies the annual energy costs and minimizes the total cost for all sets of pumping stations; the method then selects the least-cost pumps from among the feasible sets. The GA model simultaneously determines the least total annual cost of the pump station and its operation. The solution includes the selection of pump type, capacity, and the number of units, as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a set of water demand curves. Application of the two models to a real-world project shows not only considerable savings in cost and energy but also highlights the efficiency and ease of the GA approach for solving complex problems of this type.

Dynamic penalty function as a strategy in solving water resources combinatorial optimization problems with honey-bee mating optimization (HBMO) algorithm

Because of the complexity of some optimization problems, evolutionary and meta-heuristic algorithms are sometimes more applicable than the traditional optimization methods. Some difficulties in solving design-operation problems in the field of engineering are due to the multi-modality of the solution region of these problems. Since the design variables usually are specified as discrete variables and other continuous decision variables have to be set according to the range of the discrete ones, the possibility of trapping the final solution into some local optimum increases. In such cases, the capability of both traditional and evolutionary algorithms decreases. Thus, the development of a strategy to overcome this problem is the subject of this paper. For water utilities, one of the greatest potential areas for energy cost-savings is the effective scheduling of daily pump operations. Optimum design operation of pumping stations is a potential problem in this area that performs a wide background of solutions to this problem with different methods. Computation in all methods is driven by an objective function that includes operating and capital costs subject to various performances and hydraulic constraints. This paper achieves the optimal control and operation of an irrigation pumping station system by one of the latest tools used in optimization problems, which is the honey-bees mating optimization (HBMO) algorithm and is tested with a practical design. The HBMO algorithm with dynamic penalty function is presented and compared with two other well-known optimization tools which are the Lagrange multipliers (LM) method and genetic algorithms (GA) as well as with the previous results of the HBMO algorithm with constant penalty function for the same problem. The LM, GA and HBMO approaches simultaneously determine the least total annual cost of the pumping station and its operation. The solution includes the selection of pump type, capacity and the number of units, as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a set of water demand curves. In this paper, the HBMO algorithm is applied and the dynamic penalty function is tested to demonstrate the efficiency of this combination simultaneously. The results are very promising and prove the ability of combining the dynamic penalty function with the HBMO algorithm for solving combinatorial design–operation optimization problems. Application of all these models to a real-world project shows not only considerable savings in cost and energy but also highlights the efficiency and capability of the dynamic penalty function in the HBMO algorithm for solving complex problems of this type.

Evolutionary multi-objective optimization of the design and operation of water distribution network: total cost vs. reliability vs. water quality

An expanded rehabilitation of the hypothetical water distribution network of Anytown, USA is considered. As well as pipe rehabilitation decisions, tank sizing, tank siting and pump operation schedules are considered as design variables. Inclusion of pump operation schedules requires consideration of water system operation over the demand pattern period. Design of distribution storage facilities involves solving numerous issues and trade-offs such as locations, levels and volume. This paper investigates the application of multi-objective evolutionary algorithms in the identification of the pay-off characteristic between total cost, reliability and water quality of Anytown's water distribution system. A new approach is presented for formulation of the model. To provide flexibility, the network must be designed and operated under multiple loading conditions. The cost of the solution includes the capital costs of pipes and tanks as well as the present value of the energy consumed during a specified period. Optimization tends to reduce costs by reducing the diameter of, or completely eliminating, pipes, thus leaving the system with insufficient capacity to respond to pipe breaks or demands that exceed design values without violating required performance levels. Here a resilience index is considered as a second objective to increase the hydraulic reliability and the availability of water during pipe failures. Considering reliability as one of the objectives in the optimization process will decrease the level of vulnerability for the solutions and therefore will result in robust networks. However, oversized distribution mains and storage tanks will have adverse effects on water age with negative effects on water quality due to low flow velocity and little turnover, respectively. Therefore, another objective in the design and operation of distribution systems with storage facilities is the minimization of residence time, thus minimizing deterioration in water quality, which is directly associated with the age of water. Residence time must include not only the time in tanks but also the travel time before and after the water's entry into the storage facilities. The residence time of the water in the network is considered as a surrogate measure of water quality. Results are presented for the pay-off characteristics between total cost, reliability and water quality, for 24 h design and five loading conditions.

Trade-off between Total Cost and Reliability for Anytown Water Distribution Network

This paper investigates the application of multiobjective evolutionary algorithms to the identification of the payoff characteristic between total cost and reliability of a water distribution system using the well-known Anytown network as an example. An expanded rehabilitation problem is considered where the design variables are the pipe rehabilitation decisions, tank sizing, tank siting, and pump operation schedules. To provide flexibility, the network is designed and operated under multiple loading conditions. Inclusion of pump operation schedules requires consideration of water system operation over an extended period. The cost of the solution includes the capital costs of pipes and tanks as well as the present value of the energy consumed during a specified period. Optimization tends to reduce costs by reducing the diameter of, or completely eliminating, some pipes, thus leaving the system with insufficient capacity to respond to pipe breaks or demands that exceed design values without violating required performance levels. A resilience index is considered as a second objective to increase the hydraulic reliability and availability of water during pipe failures. Sensitivity analysis of solutions on the payoff curve generated by twin-objective optimization shows poor performance of these networks under random pipe failure or a pump being out of service. The minimum surplus head is added as a third objective to overcome the shortcomings of the resilience index. Results are presented for the payoff characteristics between total cost and reliability for 24 h design and five loading conditions.

Use of Genetic Algorithm in Optimization of Irrigation Pumping Stations

Energy costs constitute the largest expenditure for nearly all water utilities worldwide and can consume up to 65% of a water utility’s annual operating budget. One of the greatest potential areas for energy cost savings is in the scheduling of pump operations. This paper presents a new management model, WAPIRRA Scheduler, for the optimal design and operation of water distribution systems. The model makes use of the latest advances in genetic algorithm (GA) optimization to automatically determine annually the least cost of pumping stations while satisfying target hydraulic performance requirements. Optimal design and operation refers to selecting pump type, capacity, and number of units as well as scheduling the operation of irrigation pumps that results in minimum design and operating cost for a given set of demand curves. The optimization process consists of three main steps: (1) generating randomly an initial set of pump combinations to start the optimization process for a given demand-duration curve; ...

Optimal Pump Operations Considering Pump Switches

A methodology for determining optimal pump operation schedules for waterdistribution systems is presented. In addition to minimizing the energyconsumption cost, the model includes a constraint to l...