Browsing by Author "Chu, Hone-Jay"

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Application of Optimal Control and Fuzzy Theory for Dynamic Groundwater Remediation Design
(2009-03-01)
Chu, Hone-Jay
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Chang, Liang-Cheng
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土木工程學系
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Department of Civil Engineering
Obtaining optimal solutions for time-varying groundwater remediation design is a challenging task. A novel procedure first employs input/output data sets obtained by constrained differential dynamic programming (CDDP). Then the Adaptive-Network-Based Fuzzy Inference System (ANFIS), which is a fuzzy inference system (FIS) implemented in the adaptive network framework, is applied to acquire time-varying pumping rates. Results demonstrate that the FIS is an efficient way of groundwater remediation design.
Application of System Dynamics on Shallow Multipurpose Artificial Lakes: A Case Study of Detention Pond at Tainan, Taiwan
(2010-06-01)
Chu, Hone-Jay
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Chang, Liang-Cheng
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Lin, Yu-Pin
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Wang, Yung-Chieh
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Chen, Yu-Wen
This study designs a multipurpose urban shallow artificial lake, including water supply, flood detention, and water environment preservation. It is expected to not only preserve a healthy water environment but to also retain water conservation and flood detention. This study adopts system dynamics (SD) to analyze the relationship between different purposes of water resources utilization. Furthermore, different operation strategies effects can be simulated by SD through a proposed urban multipurpose shallow artificial lake system. The results demonstrate the dynamic effects of strategies managers propose such as demand analysis, inflow control, and water quality improvement in this case study for Taiwan. SD aids lake system prediction and understanding temporally in sequential planning for water supply, environmental preservation, and flood detention. The SD model will hopefully serve as a reference to study different features before artificial lakes constructing.
Applying Particle Swarm Optimization to Parameter Estimation of the Nonlinear Muskingum Model
(2009-09-01)
Chu, Hone-Jay
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Chang, Liang-Cheng
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土木工程學系
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Department of Civil Engineering
The Muskingum model is the most widely used method for flood routing in hydrologic engineering. However, the application of the model still suffers from a lack of an efficient method for parameter estimation. Particle swarm optimization (PSO) is applied to the parameter estimation for the nonlinear Muskingum model. PSO does not need any initial guess of each parameter and thus avoids the subjective estimation usually found in traditional estimation methods and reduces the likelihood of finding a local optimum of the parameter values. Simulation results indicate that the proposed scheme can improve the accuracy of the Muskingum model for flood routing. A case study is presented to demonstrate that the proposed scheme is an alternative way to estimate the parameters of the Muskingum model.
Integration of Optimal Dynamic Control and Neural Network for Groundwater Quality Management
(2012-03-01)
Chang, Liang-Cheng
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Chu, Hone-Jay
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Hsiao, Chin-Tsai
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土木工程學系
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Department of Civil Engineering
This study integrates an artificial neural network (ANN) and constrained differential dynamic programming (CDDP) to search for optimal solutions to a nonlinear time-varying groundwater remediation-planning problem. The proposed model (ANN-CDDP) determines optimal dynamic pumping schemes to minimize operating costs and meet water quality requirements. The model uses two embedded ANNs, including groundwater flow and contaminant transport models, as transition functions to predict groundwater levels and contaminant concentrations under time-varying pumping. Results demonstrate that ANN-CDDP is a simplified management model that requires considerably less computation time to solve a fine mesh problem. For example, the ANN-CDDP computing time for a case involving 364 nodes is 1/26.5 that of the conventional optimization model.
Optimal control algorithm and neural network for dynamic groundwater management
(2009-09-15)
Chu, Hone-Jay
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Chang, Liang-Cheng
;
土木工程學系
;
Department of Civil Engineering
Researchers have found that obtaining optimal solutions for groundwater resource-planning problems, while simultaneously considering time-varying pumping rates, is a challenging task. This study integrates any artificial neural network (ANN) and constrained differential dynamic programming (CDDP) as simulation-optimization model, called ANN-CDDP. Optimal solutions for a groundwater resource-planning problem are determined while simultaneously considering time-varying pumping rates. A trained ANN is used as the transition function to predict ground water table under variable pumping conditions. The results show that the ANN-CDDP reduces computational time by as much as 94.5% when compared to the time required by the conventional model. The proposed optimization model saves a considerable amount of computational time for solving large-scale problems. Copyright (c) 2009 John Wiley & Sons, Ltd.
Optimal planning of a dynamic pump-treat-inject groundwater remediation system
(2007-09-01)
Chang, Liang-Cheng
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Chu, Hone-Jay
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Hsiao, Chin-Tsai
;
土木工程學系
;
Department of Civil Engineering
This study integrates the genetic algorithm (GA) and constrained differential dynamic programming (CDDP) to design the pump-treat-inject system. The proposed model. considers both the cost of installing wells (fixed cost) and the operating cost of pumping, injection and water treatment. To minimize the total cost white meeting the water quality constraints, the model can compute the optimal number and locations of wells, as welt as the associated optimal pumping and injection schemes. Various numerical cases reveal that the requirement to balance the total volume between pumping and injection can significantly influence the final optimal design. (C) 2007 Elsevier B.V. All rights reserved.
Optimizing Capacity-Expansion Planning of Groundwater Supply System between Cost and Subsidence
(2010-08-01)
Chu, Hone-Jay
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Chang, Liang-Cheng
;
土木工程學系
;
Department of Civil Engineering
This work solves an optimal capacity-expansion planning problem with land subsidence constraints using a hybrid algorithm that combines the genetic algorithm (GA) and constrained differential dynamic programming (CDDP). The main structure of the hybrid algorithm is the GA, in which each chromosome represents a possible network design and its expansion schedule. The present fixed cost of each chromosome is computed easily using the GA, and CDDP is then used to solve the optimal pumping rates and compute the optimal present operating costs associated with the chromosome. Simulation results indicate that the well network has lower total present cost with the capacity-expansion system instead of installing the full system capacity at the beginning. However, the well network designed by the capacity-expansion model without considering land subsidence may induce more local land subsidence than that determined by the conventional model demonstrating the necessity of considering land subsidence constraints in the system design. This work also investigates other important issues related to the optimal design of the system-installing schedule. The proposed model is highly promising for facilitating a cost-efficient well system design for regional groundwater supply and environmental conservation.
Using a hybrid approach to optimize experimental network design for aquifer parameter identification
(2010-10-01)
Chang, Liang-Cheng
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Chu, Hone-Jay
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Lin, Yu-Pin
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Chen, Yu-Wen
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土木工程學系
This research develops an optimum design model of groundwater network using genetic algorithm (GA) and modified Newton approach, based on the experimental design conception. The goal of experiment design is to minimize parameter uncertainty, represented by the covariance matrix determinant of estimated parameters. The design problem is constrained by a specified cost and solved by GA and a parameter identification model. The latter estimates optimum parameter value and its associated sensitivity matrices. The general problem is simplified into two classes of network design problems: an observation network design problem and a pumping network design problem. Results explore the relationship between the experimental design and the physical processes. The proposed model provides an alternative to solve optimization problems for groundwater experimental design.