Records with Subject: Optimization
Showing records 1 to 25 of 36. [First] Page: 1 2 Last
A Selection Method for Power Generation Plants Used for Enhanced Geothermal Systems (EGS)
Kaiyong Hu, Jialing Zhu, Wei Zhang, Xinli Lu
January 7, 2019 (v1)
Subject: Optimization
Keywords: enhanced geothermal systems, geothermal energy, optimization method, power cycle’s selection method
As a promising and advanced technology, enhanced geothermal systems (EGS) can be used to generate electricity using deep geothermal energy. In order to better utilize the EGS to produce electricity, power cycles’ selection maps are generated for people to choose the best system based on the geofluids’ temperature and dryness conditions. Optimizations on double-flash system (DF), flash-organic Rankine cycle system (FORC), and double-flash-organic Rankine cycle system (DFORC) are carried out, and the single-flash (SF) system is set as a reference system. The results indicate that each upgraded system (DF, FORC, and DFORC) can produce more net power output compared with the SF system and can reach a maximum net power output under a given geofluid condition. For an organic Rankine cycle (ORC) using R245fa as working fluid, the generated selection maps indicate that using the FORC system can produce more power than using other power cycles when the heat source temperature is below 170 °C. E... [more]
An Integer Linear Programming Model for an Ecovat Buffer
Gijs J. H. de Goeijen, Gerard J. M. Smit, Johann L. Hurink
January 7, 2019 (v1)
Subject: Optimization
Keywords: integer linear programming, Modelling, seasonal thermal storage, smart grids
An increase in the number of volatile renewables in the electricity grid enhances the imbalance of supply and demand. One promising candidate to solve this problem is to improve the energy storage. The Ecovat system is a new seasonal thermal energy storage system currently under development. In this paper, an integer linear programming model is developed to describe the behaviour and potential of this system. Furthermore, it is compared with a previously developed model, which is simplifying the behaviour of the Ecovat system much more, but is much less computationally expensive. It is shown that the new approach performs significantly better for several cases. For controlling a real Ecovat system in the future we may incorporate a number of improvements identified by our comparison analysis into the previously developed approach, which may help increase the quality of the obtained results without increasing the computational effort too much.
A Comparative Study of Multiple-Criteria Decision-Making Methods under Stochastic Inputs
Athanasios Kolios, Varvara Mytilinou, Estivaliz Lozano-Minguez, Konstantinos Salonitis
January 7, 2019 (v1)
Subject: Optimization
Keywords: analytical hierarchy process (AHP), elimination et choix traduisant la realité (ELECTRE), multi-criteria decision methods, preference ranking organization method for enrichment evaluation (PROMETHEE), stochastic inputs, support structures, technique for the order of preference by similarity to the ideal solution (TOPSIS), weighted product method (WPM), weighted sum method (WSM), wind turbine
This paper presents an application and extension of multiple-criteria decision-making (MCDM) methods to account for stochastic input variables. More in particular, a comparative study is carried out among well-known and widely-applied methods in MCDM, when applied to the reference problem of the selection of wind turbine support structures for a given deployment location. Along with data from industrial experts, six deterministic MCDM methods are studied, so as to determine the best alternative among the available options, assessed against selected criteria with a view toward assigning confidence levels to each option. Following an overview of the literature around MCDM problems, the best practice implementation of each method is presented aiming to assist stakeholders and decision-makers to support decisions in real-world applications, where many and often conflicting criteria are present within uncertain environments. The outcomes of this research highlight that more sophisticated me... [more]
Multi-Objective Sustainable Operation of the Three Gorges Cascaded Hydropower System Using Multi-Swarm Comprehensive Learning Particle Swarm Optimization
Xiang Yu, Hui Sun, Hui Wang, Zuhan Liu, Jia Zhao, Tianhui Zhou, Hui Qin
November 28, 2018 (v1)
Subject: Optimization
Keywords: comprehensive learning, hydropower reservoir system, multi-objective optimal operation, multi-swarm, Particle Swarm Optimization
Optimal operation of hydropower reservoir systems often needs to optimize multiple conflicting objectives simultaneously. The conflicting objectives result in a Pareto front, which is a set of non-dominated solutions. Non-dominated solutions cannot outperform each other on all the objectives. An optimization framework based on the multi-swarm comprehensive learning particle swarm optimization algorithm is proposed to solve the multi-objective operation of hydropower reservoir systems. Through adopting search techniques such as decomposition, mutation and differential evolution, the algorithm tries to derive multiple non-dominated solutions reasonably distributed over the true Pareto front in one single run, thereby facilitating determining the final tradeoff. The long-term sustainable planning of the Three Gorges cascaded hydropower system consisting of the Three Gorges Dam and Gezhouba Dam located on the Yangtze River in China is studied. Two conflicting objectives, i.e., maximizing h... [more]
A Multi-Point Method Considering the Maximum Power Point Tracking Dynamic Process for Aerodynamic Optimization of Variable-Speed Wind Turbine Blades
Zhiqiang Yang, Minghui Yin, Yan Xu, Zhengyang Zhang, Yun Zou, Zhao Yang Dong
November 28, 2018 (v1)
Subject: Optimization
Keywords: aerodynamic optimization, closed-loop system, maximum power point tracking (MPPT) control, multi-point method, variable-speed wind turbine (VSWT)
Due to the dynamic process of maximum power point tracking (MPPT) caused by turbulence and large rotor inertia, variable-speed wind turbines (VSWTs) cannot maintain the optimal tip speed ratio (TSR) from cut-in wind speed up to the rated speed. Therefore, in order to increase the total captured wind energy, the existing aerodynamic design for VSWT blades, which only focuses on performance improvement at a single TSR, needs to be improved to a multi-point design. In this paper, based on a closed-loop system of VSWTs, including turbulent wind, rotor, drive train and MPPT controller, the distribution of operational TSR and its description based on inflow wind energy are investigated. Moreover, a multi-point method considering the MPPT dynamic process for the aerodynamic optimization of VSWT blades is proposed. In the proposed method, the distribution of operational TSR is obtained through a dynamic simulation of the closed-loop system under a specific turbulent wind, and accordingly the m... [more]
Simultaneous Optimization of Topology and Component Sizes for Double Planetary Gear Hybrid Powertrains
Weichao Zhuang, Xiaowu Zhang, Huei Peng, Liangmo Wang
November 28, 2018 (v1)
Subject: Optimization
Keywords: energy management, hybrid electric vehicles (HEVs), optimal design methodology, topology optimization
Hybrid powertrain technologies are successful in the passenger car market and have been actively developed in recent years. Optimal topology selection, component sizing, and controls are required for competitive hybrid vehicles, as multiple goals must be considered simultaneously: fuel efficiency, emissions, performance, and cost. Most of the previous studies explored these three design dimensions separately. In this paper, two novel frameworks combining these three design dimensions together are presented and compared. One approach is nested optimization which searches through the whole design space exhaustively. The second approach is called enhanced iterative optimization, which executes the topology optimization and component sizing alternately. A case study shows that the later method can converge to the global optimal design generated from the nested optimization, and is much more computationally efficient. In addition, we also address a known issue of optimal designs: their sens... [more]
A Multi-Objective Optimization Framework for Offshore Wind Farm Layouts and Electric Infrastructures
Silvio Rodrigues, Carlos Restrepo, George Katsouris, Rodrigo Teixeira Pinto, Maryam Soleimanzadeh, Peter Bosman, Pavol Bauer
November 27, 2018 (v1)
Subject: Optimization
Keywords: design parameters, economic functions, multi-objective optimization, offshore wind farms, trade-offs, wind farm designers
Current offshore wind farms (OWFs) design processes are based on a sequential approach which does not guarantee system optimality because it oversimplifies the problem by discarding important interdependencies between design aspects. This article presents a framework to integrate, automate and optimize the design of OWF layouts and the respective electrical infrastructures. The proposed framework optimizes simultaneously different goals (e.g., annual energy delivered and investment cost) which leads to efficient trade-offs during the design phase, e.g., reduction of wake losses vs collection system length. Furthermore, the proposed framework is independent of economic assumptions, meaning that no a priori values such as the interest rate or energy price, are needed. The proposed framework was applied to the Dutch Borssele areas I and II. A wide range of OWF layouts were obtained through the optimization framework. OWFs with similar energy production and investment cost as layouts desig... [more]
Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm
Fei Lin, Shihui Liu, Zhihong Yang, Yingying Zhao, Zhongping Yang, Hu Sun
November 27, 2018 (v1)
Subject: Optimization
Keywords: braking energy, dwell time, energy saving, Genetic Algorithm, multi-train, urban rail transit
With its large capacity, the total urban rail transit energy consumption is very high; thus, energy saving operations are quite meaningful. The effective use of regenerative braking energy is the mainstream method for improving the efficiency of energy saving. This paper examines the optimization of train dwell time and builds a multiple train operation model for energy conservation of a power supply system. By changing the dwell time, the braking energy can be absorbed and utilized by other traction trains as efficiently as possible. The application of genetic algorithms is proposed for the optimization, based on the current schedule. Next, to validate the correctness and effectiveness of the optimization, a real case is studied. Actual data from the Beijing subway Yizhuang Line are employed to perform the simulation, and the results indicate that the optimization method of the dwell time is effective.
Optimal Site Selection of Electric Vehicle Charging Stations Based on a Cloud Model and the PROMETHEE Method
Yunna Wu, Meng Yang, Haobo Zhang, Kaifeng Chen, Yang Wang
November 27, 2018 (v1)
Subject: Optimization
Keywords: Analytical Network Process (ANP), charging stations for electric vehicles, cloud model, Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), site selection
The task of site selection for electric vehicle charging stations (EVCS) is hugely important from the perspective of harmonious and sustainable development. However, flaws and inadequacies in the currently used multi-criteria decision making methods could result in inaccurate and irrational decision results. First of all, the uncertainty of the information cannot be described integrally in the evaluation of the EVCS site selection. Secondly, rigorous consideration of the mutual influence between the various criteria is lacking, which is mainly evidenced in two aspects: one is ignoring the correlation, and the other is the unconscionable measurements. Last but not least, the ranking method adopted in previous studies is not very appropriate for evaluating the EVCS site selection problem. As a result of the above analysis, a Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) method-based decision system combined with the cloud model is proposed in this paper fo... [more]
A Nature-Inspired Optimization-Based Optimum Fuzzy Logic Photovoltaic Inverter Controller Utilizing an eZdsp F28335 Board
Ammar Hussein Mutlag, Azah Mohamed, Hussain Shareef
November 27, 2018 (v1)
Subject: Optimization
Keywords: eZdsp F28335, fuzzy logic controller (FLC), inverter, lightning search algorithm (LSA), photovoltaic (PV), space vector pulse width modulation (SVPWM)
Photovoltaic (PV) inverters essentially convert DC quantities, such as voltage and current, to AC quantities whose magnitude and frequency are controlled to obtain the desired output. Thus, the performance of an inverter depends on its controller. Therefore, an optimum fuzzy logic controller (FLC) design technique for PV inverters using a lightning search algorithm (LSA) is presented in this study. In a conventional FLC, the procedure for obtaining membership functions (MFs) is usually implemented using trial and error, which does not lead to satisfactory solutions in many cases. Therefore, this study presents a technique for obtaining MFs that avoids the exhaustive traditional trial-and-error procedure. This technique is implemented during the inverter design phase by generating adaptive MFs based on the evaluation results of the objective function formulated with LSA. The mean squared error (MSE) of the inverter output voltage is used as an objective function in this study. LSA optim... [more]
Research on Heat Recovery Technology for Reducing the Energy Consumption of Dedicated Ventilation Systems: An Application to the Operating Model of a Laboratory
Lian Zhang, Yu Feng Zhang
October 23, 2018 (v1)
Subject: Optimization
Keywords: energy saving, fresh air (FA), heat pipe heat exchanger (HPHX), thermal comfort
In this research, the application of heat pipes in the air handler dedicated to decoupling dehumidification from cooling to reduce energy consumption was simulated and investigated by simulations and experimental studies. The cooling load profiles and heat pipes with effectiveness of 0.45 and 0.6, respectively, were evaluated in achieving the desired space conditions and calculated hour by hour. The results demonstrated that for all examined cases, a heat pipe heat exchanger (HPHX) can be used to save over 80% of the energy during the hours of operation of air conditioning. The overall energy reduction rate was from 3.2% to 4.5% under air conditioning system conditions. It was found that the energy saving potential of a laboratory was higher than for other kinds of buildings. Therefore, the dedicated ventilation system combined with heat recovery technology can be efficiently applied to buildings, especially for laboratories in subtropical areas.
Approximating Nonlinear Relationships for Optimal Operation of Natural Gas Transport Networks
Kody Kazda, Xiang Li
October 13, 2018 (v1)
Subject: Optimization
Keywords: Compressors, Fuel Cost Minimization Problem, GAMS, Matlab, Natural Gas, Optimization
Source code for the case study presented in the paper "Approximating Nonlinear Relationships for Optimal Operation of Natural Gas Transport Networks". The case study involves solving the compressor fuel cost minimization problem (FCMP) on three simple natural gas networks. For each gas network three different formulations of the FCMP are tested: a common simplified FCMP model (FCMP_S), the novel approximation FCMP model (FCMP_N) that is developed in the paper, and a partially rigorous FCMP model (FCMP_PR) that models components of the model using their most rigorous calculations where feasible. The FCMP for each of these tests was optimized using GAMS, for which the code is provided. The accuracy of each of the three models was then assessed by comparing them to a rigorous simulation. The rigorous simulation was coded in Matlab and is provided, where separate files are used to calculate the rigorous gas pressure drop along a pipeline, and the energy input required for gas compression... [more]
Layout Optimization Design of Two Vortex Induced Piezoelectric Energy Converters (VIPECs) Using the Combined Kriging Surrogate Model and Particle Swarm Optimization Method
Xinyu An, Baowei Song, Zhaoyong Mao, Congcong Ma
October 4, 2018 (v1)
Subject: Optimization
Keywords: Computatinal Fluid Dynamics (CFD), Kriging surrogate model, lift coefficient, open circuit output voltage, Particle Swarm Optimization (PSO), piezoelectric energy converter, separation angle, vortex shedding
The layout configuration of Vortex Induced Piezoelectric Energy Converters (VIPECs) is essential to improve its overall performance. Based on the formations of migrating geese, the configuration is characterized by two nondimensionalized layout parameters. A number of sampled points for different configurations are simulated with the two-dimensional Computation Fluid Dynamics (CFD) method. The influence of layout configurations on VIPECs’ lift force and wake structure is investigated and the generated open circuit output voltage is obtained through the derived output voltage equation. The response surface model of the output voltage of both the leading VIPEC and the following VIPEC and their summation are established using the Kriging surrogate model based on the obtained simulation results. Then, optimal layout parameters are found through the Particle Swarm Optimization (PSO) algorithm, and its predicted result is compared with that of the CFD simulation. The simulation and optimizat... [more]
Fuel-Optimal Thrust-Allocation Algorithm Using Penalty Optimization Programing for Dynamic-Positioning-Controlled Offshore Platforms
Se Won Kim, Moo Hyun Kim
September 21, 2018 (v1)
Subject: Optimization
Keywords: dynamic positioning, fuel consumption, Genetic Algorithm, Optimization, penalty programming, pseudo-inverse, quadratic-programming, thrust allocation, thruster arrangement, turret-moored FPSO
This research, a new thrust-allocation algorithm based on penalty programming is developed to minimize the fuel consumption of offshore vessels/platforms with dynamic positioning system. The role of thrust allocation is to produce thruster commands satisfying required forces and moments for position-keeping, while fulfilling mechanical constraints of the control system. The developed thrust-allocation algorithm is mathematically formulated as an optimization problem for the given objects and constraints of a dynamic positioning system. Penalty programming can solve the optimization problems that have nonlinear object functions and constraints. The developed penalty-programming thrust-allocation method is implemented in the fully-coupled vessel⁻riser⁻mooring time-domain simulation code with dynamic positioning control. Its position-keeping and fuel-saving performance is evaluated by comparing with other conventional methods, such as pseudo-inverse, quadratic-programming, and genetic-alg... [more]
Enhancing the Robustness of the Wireless Power Transfer System to Uncertain Parameter Variations Using an Interval-Based Uncertain Optimization Method
Yanting Luo, Yongmin Yang, Xisen Wen, Ming Cheng
September 21, 2018 (v1)
Subject: Optimization
Keywords: interval-based uncertain optimization, robustness, tradeoff decision, uncertain parameter variations, wireless power transfer
Uncertainty commonly exists in the wireless power transfer (WPT) systems for moving objects. To enhance the robustness of the WPT system to uncertain parameter variations, a modified WPT system structure and an interval-based uncertain optimization method are proposed in this paper. The modified WPT system, which includes two Q-type impedance matching networks, can switch between two different operating modes. The interval-based uncertain optimization method is used to improve the robustness of the modified WPT system: First, two interval-based objective functions (mean function and variance function) are defined to evaluate the average performance and the robustness of the system. A double-objective uncertain optimization model for the modified WPT system is built. Second, a bi-level nested optimization algorithm is proposed to find the Pareto optimal solutions of the proposed optimization model. The Pareto fronts are provided to illustrate the tradeoff between the two objectives, and... [more]
Energy Consumption Optimization for the Formation of Multiple Robotic Fishes Using Particle Swarm Optimization
Dong Xu, Luo Yu, Zhiyu Lv, Jiahuang Zhang, Di Fan, Wei Dai
September 21, 2018 (v1)
Subject: Optimization
Keywords: energy consumption, leader-follower formation flocking, parameter optimization, robotic fish
The traditional leader-follower formation algorithm can realize the formation of multiply robotic fishes, but fails to consider the energy consumption during the formation. In this paper, the energy optimized leader-follower formation algorithm has been investigated to solve this problem. Considering that the acceleration of robotic fish is tightly linked to the motion state and energy consumption, we optimize the corresponding control parameters of the acceleration to reduce energy consumption during the formation via particle swarm algorithm. The whole process has been presented as follows: firstly we realize the formation on the base of the kinematic model with leader-follower formation algorithm; then the energy consumption on the base of dynamical model are derived; finally we seek the optimal control parameters based on the particle swarm optimization (PSO) algorithm. The dynamics simulation of the energy optimization scheme is conducted to verify the functionality of the propose... [more]
A Novel Multi-Population Based Chaotic JAYA Algorithm with Application in Solving Economic Load Dispatch Problems
Jiangtao Yu, Chang-Hwan Kim, Abdul Wadood, Tahir Khurshiad, Sang-Bong Rhee
September 20, 2018 (v1)
Subject: Optimization
Keywords: chaos optimization algorithm (COA), economic load dispatch problem (ELD), JAYA algorithm, multi-population method (MP), optimization methods
The economic load dispatch (ELD) problem is an optimization problem of minimizing the total fuel cost of generators while satisfying power balance constraints, operating capacity limits, ramp-rate limits and prohibited operating zones. In this paper, a novel multi-population based chaotic JAYA algorithm (MP-CJAYA) is proposed to solve the ELD problem by applying the multi-population method (MP) and chaotic optimization algorithm (COA) on the original JAYA algorithm to guarantee the best solution of the problem. MP-CJAYA is a modified version where the total population is divided into a certain number of sub-populations to control the exploration and exploitation rates, at the same time a chaos perturbation is implemented on each sub-population during every iteration to keep on searching for the global optima. The proposed MP-CJAYA has been adopted to ELD cases and the results obtained have been compared with other well-known algorithms reported in the literature. The comparisons have i... [more]
Optimization of Coke Oven Gas Desulphurization and Combined Cycle Power Plant Electricity Generation
LINGYAN DENG, Thomas A. Adams II
September 12, 2018 (v3)
Subject: Optimization
Keywords: carbon tax, coke oven gas valorization, combined cycle power plant, desulphurization, net present value, Optimization, steel refinery
Many steel refineries generate significant quantities of coke oven gas (COG), which is in some cases used only to generate low pressure steam and small amounts of electric power. In order to improve energy efficiency and reduce net greenhouse gas emissions, a combined cycle power plant (CCPP) where COG is used as fuel is proposed. However, desulphurization is necessary before the COG can be used as a fuel input for CCPP. Using a local steel refinery as a case study, a proposed desulphurization process is designed to limit the H2S content in COG to less than 1 ppmv, and simulated using ProMax. In addition, the proposed CCPP plant is simulated in Aspen Plus and is optimized using GAMS to global optimality with net present value as the objective function. Furthermore, carbon tax is considered in this study. The optimized CCPP plant was observed to generate more than twice the electrical efficiency when compared to the status quo for the existing steel refinery. Thus, by generating more e... [more]
很多炼钢厂排放大量焦炉煤气。大部分焦炉煤气被用于燃烧来生产低压蒸汽以及通过汽轮机生产少量的电。为了提高发电效率并减少温室效应,本文提出运用联合循环发电来替代蒸汽发电。不同于现有的蒸汽发电的是,在联合循环发电过程中,焦炉煤气必须经过脱硫处理。基于当地炼钢厂的情况,本文提出并设计了焦炉煤气脱硫方案,使得焦炉煤气中H2S含量低于1 ppmv。该脱硫过程采用ProMax模拟。联合循环发电采用Aspen Plus模拟。并且整个联合循环发电过程又用GAMS软件模拟,以最大化纯现价为目标来优化整个联合循环发电过程。本文还考虑了二氧化碳排放税对纯现价的影响。优化后的联合循环发电效率是现有的低压蒸汽发电的两倍多。因此,通过提高发电效率,钢铁厂所需购买电量降低,也因而从生命周期的角度来说大大减少了二氧化碳的排放量。
Dynamic Optimization of a Subcritical Steam Power Plant Under Time-Varying Power Load
Chen Chen, George M. Bollas
August 28, 2018 (v1)
Subject: Optimization
Keywords: dynamic optimization, dynamic simulation, power plants, supervisory control
The increasing variability in power plant load in response to a wildly uncertain electricity market and the need to to mitigate CO₂ emissions, lead power plant operators to explore advanced options for efficiency optimization. Model-based, system-scale dynamic simulation and optimization are useful tools in this effort and are the subjects of the work presented here. In prior work, a dynamic model validated against steady-state data from a 605 MW subcritical power plant was presented. This power plant model was used as a test-bed for dynamic simulations, in which the coal load was regulated to satisfy a varying power demand. Plant-level control regulated the plant load to match an anticipated trajectory of the power demand. The efficiency of the power plant’s operation at varying loads was optimized through a supervisory control architecture that performs set point optimization on the regulatory controllers. Dynamic optimization problems were formulated to search for optimal time-varyi... [more]
GEKKO Optimization Suite
Logan D. R. Beal, Daniel C. Hill, R. Abraham Martin, John D. Hedengren
August 28, 2018 (v1)
Subject: Optimization
Keywords: algebraic modeling language, dynamic optimization, Model Predictive Control, moving horizon estimation
This paper introduces GEKKO as an optimization suite for Python. GEKKO specializes in dynamic optimization problems for mixed-integer, nonlinear, and differential algebraic equations (DAE) problems. By blending the approaches of typical algebraic modeling languages (AML) and optimal control packages, GEKKO greatly facilitates the development and application of tools such as nonlinear model predicative control (NMPC), real-time optimization (RTO), moving horizon estimation (MHE), and dynamic simulation. GEKKO is an object-oriented Python library that offers model construction, analysis tools, and visualization of simulation and optimization. In a single package, GEKKO provides model reduction, an object-oriented library for data reconciliation/model predictive control, and integrated problem construction/solution/visualization. This paper introduces the GEKKO Optimization Suite, presents GEKKO’s approach and unique place among AMLs and optimal control packages, and cites several example... [more]
Simultaneous Energy and Water Optimisation in Shale Exploration
Doris Oke, Thokozani Majozi, Rajib Mukherjee, Debalina Sengupta, Mahmoud M. El-Halwagi
July 31, 2018 (v1)
Subject: Optimization
Keywords: Energy, hydraulic fracturing, membrane distillation, optimisation, Water
This work presents a mathematical model for the simultaneous optimisation of water and energy usage in hydraulic fracturing using a continuous time scheduling formulation. The recycling/reuse of fracturing water is achieved through the purification of flowback wastewater using thermally driven membrane distillation (MD). A detailed design model for this technology is incorporated within the water network superstructure in order to allow for the simultaneous optimisation of water, operation, capital cost, and energy used. The study also examines the feasibility of utilising the co-produced gas that is traditionally flared as a potential source of energy for MD. The application of the model results in a 22.42% reduction in freshwater consumption and 23.24% savings in the total cost of freshwater. The membrane thermal energy consumption is in the order of 244 × 10³ kJ/m³ of water, which is found to be less than the range of thermal consumption values reported for membrane distillation in... [more]
Optimal Multiscale Capacity Planning in Seawater Desalination Systems
Hassan Baaqeel, Mahmoud M. El-Halwagi
July 31, 2018 (v1)
Subject: Optimization
Keywords: desalination, membrane distillation, multi-effect distillation, Optimization, process integration, Scheduling
The increasing demands for water and the dwindling resources of fresh water create a critical need for continually enhancing desalination capacities. This poses a challenge in distressed desalination network, with incessant water demand growth as the conventional approach of undertaking large expansion projects can lead to low utilization and, hence, low capital productivity. In addition to the option of retrofitting existing desalination units or installing additional grassroots units, there is an opportunity to include emerging modular desalination technologies. This paper develops the optimization framework for the capacity planning in distressed desalination networks considering the integration of conventional plants and emerging modular technologies, such as membrane distillation (MD), as a viable option for capacity expansion. The developed framework addresses the multiscale nature of the synthesis problem, as unit-specific decision variables are subject to optimization, as well... [more]
An Optimization Scheme for Water Pump Control in Smart Fish Farm with Efficient Energy Consumption
Israr Ullah, DoHyeun Kim
July 31, 2018 (v1)
Subject: Optimization
Keywords: Energy Efficiency, fish farm, IoT, Kalman filter, Optimization
Healthy fish production requires intensive care and ensuring stable and healthy production environment inside the farm tank is a challenging task. An Internet of Things (IoT) based automated system is highly desirable that can continuously monitor the fish tanks with optimal resources utilization. Significant cost reduction can be achieved if farm equipment and water pumps are operated only when required using optimization schemes. In this paper, we present a general system design for smart fish farms. We have developed an optimization scheme for water pump control to maintain desired water level in fish tank with efficient energy consumption through appropriate selection of pumping flow rate and tank filling level. Proposed optimization scheme attempts to achieve a trade-off between pumping duration and flow rate through selection of optimized water level. Kalman filter algorithm is applied to remove error in sensor readings. We observed through simulation results that optimization sc... [more]
Efficient Control Discretization Based on Turnpike Theory for Dynamic Optimization
Ali M. Sahlodin, Paul I. Barton
July 31, 2018 (v1)
Subject: Optimization
Keywords: adaptive discretization, control parametrization, dynamic optimization, optimal control, turnpike theory
Dynamic optimization offers a great potential for maximizing performance of continuous processes from startup to shutdown by obtaining optimal trajectories for the control variables. However, numerical procedures for dynamic optimization can become prohibitively costly upon a sufficiently fine discretization of control trajectories, especially for large-scale dynamic process models. On the other hand, a coarse discretization of control trajectories is often incapable of representing the optimal solution, thereby leading to reduced performance. In this paper, a new control discretization approach for dynamic optimization of continuous processes is proposed. It builds upon turnpike theory in optimal control and exploits the solution structure for constructing the optimal trajectories and adaptively deciding the locations of the control discretization points. As a result, the proposed approach can potentially yield the same, or even improved, optimal solution with a coarser discretization... [more]
Optimization of Stimulation Parameters for Targeted Activation of Multiple Neurons Using Closed-Loop Search Methods
Michelle L. Kuykendal, Stephen P. DeWeerth, Martha A. Grover
July 31, 2018 (v1)
Subject: Optimization
Keywords: closed-loop, dissociated culture, extracellular electrical stimulation, feedback, micro-electrode array (MEA), Optimization, Powell
Differential activation of neuronal populations can improve the efficacy of clinical devices such as sensory or cortical prostheses. Improving stimulus specificity will facilitate targeted neuronal activation to convey biologically realistic percepts. In order to deliver more complex stimuli to a neuronal population, stimulus optimization techniques must be developed that will enable a single electrode to activate subpopulations of neurons. However, determining the stimulus needed to evoke targeted neuronal activity is challenging. To find the most selective waveform for a particular population, we apply an optimization-based search routine, Powell’s conjugate direction method, to systematically search the stimulus waveform space. This routine utilizes a 1-D sigmoid activation model and a 2-D strength⁻duration curve to measure neuronal activation throughout the stimulus waveform space. We implement our search routine in both an experimental study and a simulation study to characterize... [more]
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