Simulation and optimization of groundwater exploitation for the water supply of Tarnów agglomeration (southern Poland)

Abstract This paper presents the application of the simulation-optimization approach to optimize groundwater intake for the water supply of Tarnów agglomeration (southern Poland). Tarnów agglomeration is supplied with water from extensively exploited surface and groundwater intakes located at the confluence of the rivers Dunajec and Biała. Groundwater intakes capture water from the Quaternary aquifer, which is recharged by rainfall and direct infiltration from the rivers and irrigation ditches. Hydrogeological conditions occurring within the area under consideration were mapped by using a single-layer numerical model in Processing Modflow software. After calibrating the simulation model, a simulation-optimization approach was developed with the aim of aiding the process of searching for the best scheme of exploitation of groundwater intake. The unit response matrix method was used to connect the simulation model with the optimization procedure. In the optimization task the objective functions concerning the total volume of groundwater intake discharge and infiltration amount from the River Dunajec or irrigation ditches were applied. Several constraints concerned both the maximum and minimum capacity of individual wells and the entire intake, as well as desirable or undesirable drawdown values in selected control points and control areas. Finally, twelve variants of optimization tasks, for different boundary conditions and different objective functions, were solved and optimal schemes of well discharge distribution within the intake were calculated. The results of optimization show that, depending on the intended objective and the constraints imposed, the best exploitation scenario under the given conditions may be found.

Download Full-text

Experiences in Modeling, Simulation, and Optimization of Chemical Industry Supply Chains

Manufacturing ◽

10.1115/imece2002-33606 ◽

2002 ◽

Author(s):

Charles R. Standridge ◽

David R. Heltne

Keyword(s):

Supply Chain ◽

Simulation Model ◽

Production Planning ◽

Simulation Optimization ◽

Process Industry ◽

Plant Production ◽

Optimization Models ◽

Individual Plant ◽

Simulation And Optimization ◽

Combined Simulation

We have developed and applied simulation as well as combined simulation – optimization models to represent process industry plant logistics and supply chain operations. The simulation model represents plant production, inventory, and shipping operations as well as inter-plant shipments. When a combined simulation-optimization approach is used, the simulation periodically invokes a classical production planning optimization model to set production and shipping levels. These levels are retrieved by and used in the simulation model. Process industry supply chain operations include stochastic elements such as customer demands whose expected values may vary in time as well as transportation lead times. The complexity of individual plant operations and logistics must be considered. Simulation provides the methods needed to integrate these elements in a single model. Periodically during a simulation run, production planning decisions that require optimization models may be made. Simulation experimental results are used to determine service levels to end customers as well as to set rail fleet sizes, inventory capacities, and capital equipment requirements for logistics as well as to assess alternative shipping schedules.

Download Full-text

Simulation Optimization for Complex Multi-Domain Physical Systems Based on Partial Resolving

Processes ◽

10.3390/pr7060334 ◽

2019 ◽

Vol 7 (6) ◽

pp. 334

Author(s):

Kexi Hou ◽

Yaohui Li

Keyword(s):

Simulation Model ◽

Parameter Optimization ◽

Iterative Process ◽

Simulation Optimization ◽

Differential Algebraic Equations ◽

Optimization Process ◽

Algebraic Equations ◽

Objective Functions ◽

Complex Simulation ◽

Solving Strategy

The iterative process of simulation optimization is a time-consuming task, as it involves executing the main simulation program in order to evaluate the optimal constraints and objective functions repeatedly according to the values of tuner parameters. Parameter optimization for a model of a multi-domain physical system based on Modelica is a typical simulation optimization problem. Traditionally, each simulation during each iterative step needs resolve all the variables in all the mass differential-algebraic equations (DAE) generated from the simulation model through constructing and traversing the solving dependency graph of the model. In order to improve the efficiency of the simulation optimization process, a new method named partial simulation resolving algorithm based on the set of input parameters and output variables for complex simulation model was proposed. By using this algorithm, a minimum solving graph (MSG) of the simulation model was built according to the set of parameters, constraints, and objective functions of the optimization model. The simulation during the optimization iterative process needs only to resolve the variables on the MSG, and therefore this method could decrease the simulation time greatly during every iterative step of the optimization process. As an example, the parameter optimization on economy of fuel for a heavy truck was realized to demonstrate the efficiency of this solving strategy. This method has been implemented in MWorks—a Modelica-based simulation platform.

Download Full-text

Simulation-Optimization for Conjunctive Water Resources Management and Optimal Crop Planning in Kushabhadra-Bhargavi River Delta of Eastern India

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17103521 ◽

2020 ◽

Vol 17 (10) ◽

pp. 3521

Author(s):

Madan K. Jha ◽

Richard C. Peralta ◽

Sasmita Sahoo

Keyword(s):

Water Resources ◽

Simulation Model ◽

Seawater Intrusion ◽

Optimization Model ◽

Crop Yields ◽

Simulation Optimization ◽

Optimization Models ◽

Water Model ◽

Cropping Pattern ◽

Groundwater Exploitation

Water resources sustainability is a worldwide concern because of climate variability, growing population, and excessive groundwater exploitation in order to meet freshwater demand. Addressing these conflicting challenges sometimes can be aided by using both simulation and mathematical optimization tools. This study combines a groundwater-flow simulation model and two optimization models to develop optimal reconnaissance-level water management strategies. For a given set of hydrologic and management constraints, both of the optimization models are applied to part of the Mahanadi River basin groundwater system, which is an important source of water supply in Odisha State, India. The first optimization model employs a calibrated groundwater simulation model (MODFLOW-2005, the U.S. Geological Survey modular ground-water model) within the Simulation-Optimization MOdeling System (SOMOS) module number 1 (SOMO1) to estimate maximum permissible groundwater extraction, subject to suitable constraints that protect the aquifer from seawater intrusion. The second optimization model uses linear programming optimization to: (a) optimize conjunctive allocation of surface water and groundwater and (b) to determine a cropping pattern that maximizes net annual returns from crop yields, without causing seawater intrusion. Together, the optimization models consider the weather seasons, and the suitability and variability of existing cultivable land, crops, and the hydrogeologic system better than the models that do not employ the distributed maximum groundwater pumping rates that will not induce seawater intrusion. The optimization outcomes suggest that minimizing agricultural rice cultivation (especially during the non-monsoon season) and increasing crop diversification would improve farmers’ livelihoods and aid sustainable use of water resources.

Download Full-text

Improving parcel transshipment operations - impact of different objective functions in a combined simulation and optimization approach

2015 Winter Simulation Conference (WSC) ◽

10.1109/wsc.2015.7408309 ◽

2015 ◽

Cited By ~ 3

Author(s):

Uwe Clausen ◽

Daniel Diekmann ◽

Jens Baudach ◽

Jan Kaffka ◽

Moritz Poting

Keyword(s):

Optimization Approach ◽

Objective Functions ◽

Simulation And Optimization ◽

Combined Simulation

Download Full-text

Planning for infrastructure capacity expansion of urban water supply portfolios with an integrated simulation-optimization approach

Sustainable Cities and Society ◽

10.1016/j.scs.2016.11.003 ◽

2017 ◽

Vol 29 ◽

pp. 247-256 ◽

Cited By ~ 9

Author(s):

Caetano C.S. Fraga ◽

Josué Medellín-Azuara ◽

Guilherme F. Marques

Keyword(s):

Water Supply ◽

Simulation Optimization ◽

Capacity Expansion ◽

Optimization Approach ◽

Urban Water ◽

Urban Water Supply ◽

Integrated Simulation

Download Full-text

A New Simulation-Optimization Approach Using Hybrid Radial Basis Function and Particle Swarm Optimization in Multi-Transmitter Placement Planning

10.20944/preprints202009.0164.v1 ◽

2020 ◽

Author(s):

Amir Parnianifard ◽

Muhammad Saadi ◽

Manus Pengnoo ◽

Muhammad Ali Imran ◽

Lunchakorn Wuttisittikulkij

Keyword(s):

Radial Basis Function ◽

Basis Function ◽

Simulation Optimization ◽

Particle Swarm ◽

Optimization Procedure ◽

Base Station ◽

Base Stations ◽

Optimization Approach ◽

Particle Swarm Optimizer ◽

Radial Basis

With the every passing day, the demand for data traffic is increasing and this demand forces the research community not only to look for alternating spectrum for communication but also urges the radio frequency planners to use the existing spectrum smartly. Cell size is shrinking with the every upcoming communication generation which makes the base station placement planning complex and cumbersome. In order to make the next-generation cost-effective, it is important to design the network in such a way which utilizes minimum number of base stations while ensure coverage and quality of service. This paper aims at develop a new approach using hybrid metaheuristic and metamodel applied in multi-transmitter placement planning (MTPP) problem. We apply radial basis function (RBF) metamodel to assist particle swarm optimizer (PSO) in a constrained simulation-optimization (SO) of MTPP to mitigate the associated computational burden of optimization procedure. We evaluate the effectiveness and applicability of proposed algorithm in a case study by simulating MTPP model with two, three, four and five transmitters.

Download Full-text

A simulation/optimization model for selecting infrastructure alternatives in complex water resource systems

Water Science & Technology ◽

10.2166/wst.2010.220 ◽

2010 ◽

Vol 61 (12) ◽

pp. 3050-3060 ◽

Cited By ~ 5

Author(s):

Claudio Arena ◽

Mario Rosario Mazzola ◽

Giuseppe Scordo

Keyword(s):

Water Resources ◽

Simulation Model ◽

Model Simulation ◽

Simulation Optimization ◽

Cost Benefit ◽

Optimization Procedure ◽

Bulk Water ◽

Water Resources System ◽

Nonnegativity Constraints ◽

Western Sicily

The paper introduces a simulation/optimization procedure for the assessment and the selection of infrastructure alternatives in a complex water resources system, i.e. in a multisource (reservoirs) multipurpose bulk water supply scheme. An infrastucture alternative is here a vector X of n decision variables describing the candidate expansions/new plants/water transfers etc. Each parameter may take on a discrete number of values, with its own investment cost attached. The procedure uses genetic algorithms for the search of the optimal vector X through operators mimicking the mechanisms of natural selection. For each X, the value of the objective function (O.F.) is assessed via a simulation model. Simulation is necessary as the O.F. contains, besides investment costs, also incremental operation costs and benefits that depend on the incremental water amounts which the alternative can provide. The simulation model transforms a thirty-year hydrologic input at daily/monthly scale in water allocations, accounting for the usual nonnegativity constraints and using some simple, sytem-specific rules aimed at reducing spills and at sharing water deficits among demand centres. Different O.Fs and constraints have been tested, such as incremental financial cost/benefit minimization under various maximum water deficit constraints scenarios or cost/benefit mimization including scarcity costs. This latter approach has the advantage of implicitly allowing for the magnitude of deficits, but requires the assessment of deficit-scarcity cost relationships. The application of the procedure to a water resources system in south-western Sicily shows that the model is able to converge to results that are consistent with the planning options expressed by the selected O.Fs.

Download Full-text