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Records with Keyword: Optimization
Showing records 1 to 25 of 42. [First] Page: 1 2 Last
Model-Based Stochastic Fault Detection and Diagnosis of Lithium-Ion Batteries
Jeongeun Son, Yuncheng Du
April 15, 2019 (v1)
Keywords: fault detection and classification, lithium-ion battery, Optimization, polynomial chaos expansion, thermal management, uncertainty analysis
The Lithium-ion battery (Li-ion) has become the dominant energy storage solution in many applications, such as hybrid electric and electric vehicles, due to its higher energy density and longer life cycle. For these applications, the battery should perform reliably and pose no safety threats. However, the performance of Li-ion batteries can be affected by abnormal thermal behaviors, defined as faults. It is essential to develop a reliable thermal management system to accurately predict and monitor thermal behavior of a Li-ion battery. Using the first-principle models of batteries, this work presents a stochastic fault detection and diagnosis (FDD) algorithm to identify two particular faults in Li-ion battery cells, using easily measured quantities such as temperatures. In addition, models used for FDD are typically derived from the underlying physical phenomena. To make a model tractable and useful, it is common to make simplifications during the development of the model, which may con... [more]
Ultrasonic-Assisted Extraction and Swarm Intelligence for Calculating Optimum Values of Obtaining Boric Acid from Tincal Mineral
Bahdisen Gezer, Utku Kose
April 15, 2019 (v1)
Keywords: Artificial Intelligence, boric acid, central composite design, Optimization, swarm intelligence, tincal, ultrasound assisted extraction
The objective of this study is to focus on boric acid extraction from the mineral tincal, in order to determine the optimum conditions thanks to the ultrasonic-assisted extraction (UAE) technique (with the response surface methodology (RSM) for the first time), and artificial intelligence based swarm intelligence. Characterization of the tincal were done by using thermo-gravimetric assay (TG-DTA), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR) analyses. In detail, a central composite design (CCD) was used for determining the effects of different solvent/solid ratios, pH, extraction time, and extraction temperature on the yield, which was determined by the conductometric method. The optimum values regarding the best extraction process was calculated by using five different swarm intelligence techniques: Particle swarm optimization (PSO), cuckoo search (CS), genetic algorithms (GA), Differential evolution (DE), and the vortex optimization algorithm (VOA). In... [more]
Optimization of Reaction Selectivity Using CFD-Based Compartmental Modeling and Surrogate-Based Optimization
Shu Yang, San Kiang, Parham Farzan, Marianthi Ierapetritou
April 9, 2019 (v1)
Keywords: CFD-simulation, compartmental modeling, competing reaction system, Mixing, model order reduction, Optimization, surrogate-based optimization
Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology inco... [more]
FFANN Optimization by ABC for Controlling a 2nd Order SISO System’s Output with a Desired Settling Time
Aydın Mühürcü
April 9, 2019 (v1)
Keywords: ABC, buck converter, control, FFANN, Modelling, Optimization, settling time
In this study, a control strategy is aimed to ensure the settling time of a 2nd order system’s output value while its input reference value is changed. Here, Feed Forward Artificial Neural Network (FFANN) nonlinear structure has been chosen as a control algorithm. In order to implement the intended control strategy, FFANN’s normalization coefficient (K), learning coefficients (ŋ), momentum coefficients (μ) and the sampling time (Ts) were optimized by Artificial Bee Colony (ABC) but FFANN’s values of weights were chosen arbitrary on start time of control system. After optimization phase, the FFANN behaves as an adaptive optimal discrete time non-linear controller that forces the system output to take the same value with the input reference for a desired settling time (ts). The success of the optimization algorithm was proved with close loop feedback control simulations on Matlab’s Simulink platform based on 2nd order transfer functions. Also, the success was proved with a 2nd order phys... [more]
Optimization of Reducing Sugar Production from Manihot glaziovii Starch Using Response Surface Methodology
Abdi Hanra Sebayang, Masjuki Haji Hassan, Hwai Chyuan Ong, Surya Dharma, Arridina Susan Silitonga, Fitranto Kusumo, Teuku Meurah Indra Mahlia, Aditiya Harjon Bahar
March 26, 2019 (v1)
Subject: Biosystems
Keywords: alternative fuel, bioethanol, Fermentation, hydrolysis, Manihot glaziovii (M. glaziovii), Optimization
Bioethanol is known as a viable alternative fuel to solve both energy and environmental crises. This study used response surface methodology based on the Box-Behnken experimental design to obtain the optimum conditions for and quality of bioethanol production. Enzymatic hydrolysis optimization was performed with selected hydrolysis parameters, including substrate loading, stroke speed, α-amylase concentration and amyloglucosidase concentration. From the experiment, the resulting optimum conditions are 23.88% (w/v) substrate loading, 109.43 U/g α-amylase concentration, 65.44 U/mL amyloglucosidase concentration and 74.87 rpm stroke speed, which yielded 196.23 g/L reducing sugar. The fermentation process was also carried out, with a production value of 0.45 g ethanol/g reducing sugar, which is equivalent to 88.61% of ethanol yield after fermentation by using Saccharomyces cerevisiae (S. cerevisiae). The physical and chemical properties of the produced ethanol are within the specifications... [more]
Real-Time Velocity Optimization to Minimize Energy Use in Passenger Vehicles
Thomas Levermore, M. Necip Sahinkaya, Yahya Zweiri, Ben Neaves
March 26, 2019 (v1)
Keywords: dynamic programming, fuel, fuel consumption, Optimization
Energy use in internal combustion engine passenger vehicles contributes directly to CO 2 emissions and fuel consumption, as well as producing a number of air pollutants. Optimizing the vehicle velocity by utilising upcoming road information is an opportunity to minimize vehicle energy use without requiring mechanical design changes. Dynamic programming is capable of such an optimization task and is shown in simulation to produce fuel savings, on average 12%, compared to real driving data; however, in this paper it is also applied in real time on a Raspberry Pi, a low cost miniature computer, in situ in a vehicle. A test drive was undertaken with driver feedback being provided by a dynamic programming algorithm, and the results are compared to a simulated intelligent cruise control system that can follow the algorithm results precisely. An 8% reduction in fuel with no loss in time is reported compared to the test driver.
Artificial Neural Network Model for Alkali-Surfactant-Polymer Flooding in Viscous Oil Reservoirs: Generation and Application
Si Le Van, Bo Hyun Chon
February 27, 2019 (v1)
Keywords: artificial neural network, chemical flooding, enhanced oil recovery, net present value, Optimization
Chemical flooding has been widely utilized to recover a large portion of the oil remaining in light and viscous oil reservoirs after the primary and secondary production processes. As core-flood tests and reservoir simulations take time to accurately estimate the recovery performances as well as analyzing the feasibility of an injection project, it is necessary to find a powerful tool to quickly predict the results with a level of acceptable accuracy. An approach involving the use of an artificial neural network to generate a representative model for estimating the alkali-surfactant-polymer flooding performance and evaluating the economic feasibility of viscous oil reservoirs from simulation is proposed in this study. A typical chemical flooding project was referenced for this numerical study. A number of simulations have been made for training on the basis of a base case from the design of 13 parameters. After training, the network scheme generated from a ratio data set of 50%-20%-30%... [more]
Green Small Cell Operation of Ultra-Dense Networks Using Device Assistance
Gilsoo Lee, Hongseok Kim
February 27, 2019 (v1)
Keywords: belief propagation, cellular networks, Energy Efficiency, Optimization, small cell
As higher performance is demanded in 5G networks, energy consumption in wireless networks increases along with the advances of various technologies, so enhancing energy efficiency also becomes an important goal to implement 5G wireless networks. In this paper, we study the energy efficiency maximization problem focused on finding a suitable set of turned-on small cell access points (APs). Finding the suitable on/off states of APs is challenging since the APs can be deployed by users while centralized network planning is not always possible. Therefore, when APs in small cells are randomly deployed and thus redundant in many cases, a mechanism of dynamic AP turning-on/off is required. We propose a device-assisted framework that exploits feedback messages from the user equipment (UE). To solve the problem, we apply an optimization method using belief propagation (BP) on a factor graph. Then, we propose a family of online algorithms inspired by BP, called DANCE, that requires low computati... [more]
On the Reliability of Optimization Results for Trigeneration Systems in Buildings, in the Presence of Price Uncertainties and Erroneous Load Estimation
Antonio Piacentino, Roberto Gallea, Pietro Catrini, Fabio Cardona, Domenico Panno
February 27, 2019 (v1)
Keywords: buildings, cogeneration, energy loads, linear programming, Optimization, prices, sensitivity, stochastic, trigeneration, uncertainty
Cogeneration and trigeneration plants are widely recognized as promising technologies for increasing energy efficiency in buildings. However, their overall potential is scarcely exploited, due to the difficulties in achieving economic viability and the risk of investment related to uncertainties in future energy loads and prices. Several stochastic optimization models have been proposed in the literature to account for uncertainties, but these instruments share in a common reliance on user-defined probability functions for each stochastic parameter. Being such functions hard to predict, in this paper an analysis of the influence of erroneous estimation of the uncertain energy loads and prices on the optimal plant design and operation is proposed. With reference to a hotel building, a number of realistic scenarios is developed, exploring all the most frequent errors occurring in the estimation of energy loads and prices. Then, profit-oriented optimizations are performed for the examined... [more]
Local Alternative for Energy Supply: Performance Assessment of Integrated Community Energy Systems
Binod Prasad Koirala, José Pablo Chaves Ávila, Tomás Gómez, Rudi A. Hakvoort, Paulien M. Herder
February 27, 2019 (v1)
Keywords: distributed energy resources (DERs), energy communities, multi-carrier energy systems, Optimization, smart grids
Integrated community energy systems (ICESs) are emerging as a modern development to re-organize local energy systems allowing simultaneous integration of distributed energy resources (DERs) and engagement of local communities. Although local energy initiatives, such as ICESs are rapidly emerging due to community objectives, such as cost and emission reductions as well as resiliency, assessment and evaluation are still lacking on the value that these systems can provide both to the local communities as well as to the whole energy system. In this paper, we present a model-based framework to assess the value of ICESs for the local communities. The distributed energy resources-consumer adoption model (DER-CAM) based ICES model is used to assess the value of an ICES in the Netherlands. For the considered community size and local conditions, grid-connected ICESs are already beneficial to the alternative of solely being supplied from the grid both in terms of total energy costs and CO₂ emissi... [more]
Experimental Optimization of Passive Cooling of a Heat Source Array Flush-Mounted on a Vertical Plate
Antoine Baudoin, Didier Saury, Bo Zhu, Cecilia Boström
February 5, 2019 (v1)
Subject: Other
Keywords: discrete heat sources, natural convection, Optimization, source array
Heat sources, such as power electronics for offshore power, could be cooled passively—mainly by conduction and natural convection. The obvious advantage of this strategy is its high reliability. However, it must be implemented in an efficient manner (i.e., the area needs to be kept low to limit the construction costs). In this study, the placement of multiple heat sources mounted on a vertical plate was studied experimentally for optimization purposes. We chose a regular distribution, as this is likely to be the preferred choice in the construction process. We found that optimal spacing can be determined for a targeted source density by tuning the vertical and horizontal spacing between the heat sources. The optimal aspect ratio was estimated to be around two.
Control Optimization of Solar Thermally Driven Chillers
Antoine Dalibard, Daniel Gürlich, Dietrich Schneider, Ursula Eicker
January 31, 2019 (v1)
Keywords: absorption chiller, control strategy, heat rejection, Optimization, solar cooling
Many installed solar thermally driven cooling systems suffer from high auxiliary electric energy consumption which makes them not more efficient than conventional compression cooling systems. A main reason for this is the use of non-efficient controls with constant set points that do not allow a chiller power modulation at partial-load and therefore lead to unnecessary high power consumption of the parasitics. The aims of this paper are to present a method to control efficiently solar thermally driven chillers, to demonstrate experimentally its applicability and to quantify the benefits. It has been shown that the cooling capacity of a diffusion absorption chiller can be modulated very effectively by adjusting both the temperature and the flow rate of the cooling water. With the developed approach and the use of optimization algorithms, both the temperature and the flow rate can be controlled simultaneously in a way that the cooling load is matched and the electricity consumption is mi... [more]
Analytical Model of a Dual Rotor Radial Flux Wind Generator Using Ferrite Magnets
Peifeng Xu, Kai Shi, Yuxin Sun, Huangqiu Zhu
January 31, 2019 (v1)
Keywords: analytical model, dual rotor radial flux wind generator, equivalent magnetic circuit, ferrite magnets, finite element method, Optimization
This paper presents a comprehensive analytical model for dual rotor radial flux wind generators based on the equivalent magnetic circuit method. This model is developed to predict the flux densities of the inner and outer air gaps, flux densities of the rotor and stator yokes, back electromotive force (EMF), electromagnetic torque, cogging torque, and some other characteristics important for generator design. The 2D finite element method (FEM) is employed to verify the presented analytical model, fine-tune it, and validate the prediction precision. The results show that the errors between the proposed analytical model and the FEM results are less than 5% and even less than 1% for certain parameters, that is, the results obtained from the proposed analytical model match well the ones obtained from FEM analysis. Meanwhile, the working points at different temperatures are confirmed to exceed the knee point of the BH curve, which means that irreversible demagnetization does not occur. Fina... [more]
Earliest Deadline Control of a Group of Heat Pumps with a Single Energy Source
Jiří Fink, Richard P. van Leeuwen
January 7, 2019 (v1)
Keywords: combined heat and power control, domestic hot water, floor heating, heat pump control, Model Predictive Control, Optimization, renewable energy integration, smart grids, smart control, thermal storage
In this paper, we develop and investigate the optimal control of a group of 104 heat pumps and a central Combined Heat and Power unit (CHP). The heat pumps supply space heating and domestic hot water to households. Each house has a buffer for domestic hot water and a floor heating system for space heating. Electricity for the heat pumps is generated by a central CHP unit, which also provides thermal energy to a district heating system. The paper reviews recent smart grid control approaches for central and distributed levels. An online algorithm is described based on the earliest deadline first theory that can be used on the aggregator level to control the CHP and to give signals to the heat pump controllers if they should start or should wait. The central controller requires only a limited amount of privacy-insensitive information from the heat pump controllers about their deadlines, which the heat pump controllers calculate for themselves by model predictions. In this way, a robust he... [more]
Real Time Information Based Energy Management Using Customer Preferences and Dynamic Pricing in Smart Homes
Muhammad Babar Rasheed, Nadeem Javaid, Muhammad Awais, Zahoor Ali Khan, Umar Qasim, Nabil Alrajeh, Zafar Iqbal, Qaisar Javaid
January 7, 2019 (v1)
Keywords: demand side management, energy management, genetic algorithm (GA), knapsack, microgird, Optimization, programmable communication thermostat, real time pricing, smart grid (SG)
This paper presents real time information based energy management algorithms to reduce electricity cost and peak to average ratio (PAR) while preserving user comfort in a smart home. We categorize household appliances into thermostatically controlled (tc), user aware (ua), elastic (el), inelastic (iel) and regular (r) appliances/loads. An optimization problem is formulated to reduce electricity cost by determining the optimal use of household appliances. The operational schedules of these appliances are optimized in response to the electricity price signals and customer preferences to maximize electricity cost saving and user comfort while minimizing curtailed energy. Mathematical optimization models of tc appliances, i.e., air-conditioner and refrigerator, are proposed which are solved by using intelligent programmable communication thermostat ( iPCT). We add extra intelligence to conventional programmable communication thermostat (CPCT) by using genetic algorithm (GA) to control tc a... [more]
Optimal Planning of Sustainable Buildings: Integration of Life Cycle Assessment and Optimization in a Decision Support System (DSS)
Fabio Magrassi, Adriana Del Borghi, Michela Gallo, Carlo Strazza, Michela Robba
December 3, 2018 (v1)
Keywords: decision support system (DSS), life cycle assessment (LCA), nearly-zero energy buildings, Optimization, sustainable buildings
Energy efficiency measures in buildings can provide for a significant reduction of greenhouse gas (GHG) emissions. A sustainable design and planning of technologies for energy production should be based on economic and environmental criteria. Life Cycle Assessment (LCA) is used to quantify the environmental impacts over the whole cycle of life of production plants. Optimization models can support decisions that minimize costs and negative impacts. In this work, a multi-objective decision problem is formalized that takes into account LCA calculations and that minimizes costs and GHG emissions for general buildings. A decision support system (DSS) is applied to a real case study in the Northern Italy, highlighting the advantage provided by the installation of renewable energy. Moreover, a comparison among different optimal and non optimal solution was carried out to demonstrate the effectiveness of the proposed DSS.
Online Power Management with Embedded Offline-Optimized Parameters for a Three-Source Hybrid Powertrain with an Experimental Emulation Application
Bedatri Moulik, Dirk Söffker
November 28, 2018 (v1)
Keywords: HEV, Optimization, rule-based power management
Real-time power management in the presence of one or more reversible energy storage systems is a current issue with hybrid electric vehicles (HEVs). To evaluate the potentials of rule-based power management, optimization with respect to two conflicting objectives, fuel consumption and state of charge (SoC) deviation, is considered in this contribution. A modular structure of power management with decoupled offline and online parts is presented. The online part incorporates look-up tables (LUTs) with parameters from the offline optimization part. This permits an inclusion of more LUTs corresponding to different drive patterns. The goal of this contribution is to combine the real-time applicability of rule-based power management and the multi-objective optimization property of genetic algorithms in a single control strategy. Component aging problems are addressed by suitable design. The influence of sizing is investigated. Finally, an experimental setup consisting of components capable o... [more]
Quantifying the Impact of Feedstock Quality on the Design of Bioenergy Supply Chain Networks
Krystel K. Castillo-Villar, Hertwin Minor-Popocatl, Erin Webb
November 27, 2018 (v1)
Subject: Biosystems
Keywords: bioenergy, bioethanol, Biomass, logging residues, logistics, Optimization, quality costing, supply chain network design
Logging residues, which refer to the unused portions of trees cut during logging, are important sources of biomass for the emerging biofuel industry and are critical feedstocks for the first-type biofuel facilities (e.g., corn-ethanol facilities). Logging residues are under-utilized sources of biomass for energetic purposes. To support the scaling-up of the bioenergy industry, it is essential to design cost-effective biofuel supply chains that not only minimize costs, but also consider the biomass quality characteristics. The biomass quality is heavily dependent upon the moisture and the ash contents. Ignoring the biomass quality characteristics and its intrinsic costs may yield substantial economic losses that will only be discovered after operations at a biorefinery have begun. This paper proposes a novel bioenergy supply chain network design model that minimizes operational costs and includes the biomass quality-related costs. The proposed model is unique in the sense that it suppor... [more]
Distributed Renewable Generation and Storage System Sizing Based on Smart Dispatch of Microgrids
Raji Atia, Noboru Yamada
November 27, 2018 (v1)
Keywords: demand response (DR), distributed power generation, energy management, Energy Storage, microgrid (MG), Optimization
This paper considers the contribution of independent owners (IOs) operating within microgrids (MGs) toward green power generation in deregulated energy markets. An optimization scheme is introduced for sizing distributed renewable generation (DRG) and a distributed energy storage system (DESS) based on a novel energy management system (EMS) that accounts for demand response (DR), DESS dispatch and performance degradation, dynamic pricing environments, power distribution loss and irregular renewable generation. The proposed EMS utilizes an iterative Newton-Raphson linear programming algorithm that schedules resources in order to minimize the objective function, to deal with the complicated nonlinear nature of the problem and to enable efficient long-term assessments. The EMS is used to evaluate candidate solutions that are generated by a genetic algorithm (GA) to determine the optimal combination of DRG and DESS. A case study for IEEE 34-bus distribution MG in Okinawa, Japan, is used fo... [more]
Optimal Power Management Strategy for Energy Storage with Stochastic Loads
Stefano Pietrosanti, William Holderbaum, Victor M. Becerra
November 27, 2018 (v1)
Keywords: Energy Storage, flywheel, Optimization, power management, RTG crane, stochastic loads
In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary energy source and equipped with energy storage, under the assumption that the statistical distribution of load durations is known. By including the variability of the load in the cost function, it was possible to define the optimality criteria for the power flow of the storage. Numerical calculations have been performed obtaining the control strategies associated with the global minimum in energy costs, for a wide range of initial conditions of the system. The results of the calculations have been tested on a MATLAB/Simulink model of a rubber tyre gantry (RTG) crane equipped with a flywheel energy storage system (FESS) and subjected to a test cycle, which corresponds to the real operation of a crane in the Port of Felixstowe. The... [more]
Diesel-Minimal Combustion Control of a Natural Gas-Diesel Engine
Florian Zurbriggen, Richard Hutter, Christopher Onder
October 23, 2018 (v1)
Keywords: closed-loop control, combustion control, Diesel, dual fuel, engine control, extremum seeking, internal combustion engine, Natural Gas, Optimization
This paper investigates the combustion phasing control of natural gas-diesel engines. In this study, the combustion phasing is influenced by manipulating the start and the duration of the diesel injection. Instead of using both degrees of freedom to control the center of combustion only, we propose a method that simultaneously controls the combustion phasing and minimizes the amount of diesel used. Minimizing the amount of diesel while keeping the center of combustion at a constant value is formulated as an optimization problem with an equality constraint. A combination of feedback control and extremum seeking is used to solve this optimization problem online. The necessity to separate the different time scales is discussed and a structure is proposed that facilitates this separation for this specific example. The proposed method is validated by experiments on a test bench.
Assessing the Potential of Plug-in Electric Vehicles in Active Distribution Networks
Reza Ahmadi Kordkheili, Seyyed Ali Pourmousavi, Mehdi Savaghebi, Josep M. Guerrero, Mohammad Hashem Nehrir
October 23, 2018 (v1)
Keywords: Optimization, photovoltaic (PV) panels, plug-in electric vehicle (PEV), state of charge (SoC), vehicle to grid (V2G)
A multi-objective optimization algorithm is proposed in this paper to increase the penetration level of renewable energy sources (RESs) in distribution networks by intelligent management of plug-in electric vehicle (PEV) storage. The proposed algorithm is defined to manage the reverse power flow (PF) from the distribution network to the upstream electrical system. Furthermore, a charging algorithm is proposed within the proposed optimization in order to assure PEV owner’s quality of service (QoS). The method uses genetic algorithm (GA) to increase photovoltaic (PV) penetration without jeopardizing PEV owners’ (QoS) and grid operating limits, such as voltage level of the grid buses. The method is applied to a part of the Danish low voltage (LV) grid to evaluate its effectiveness and capabilities. Different scenarios have been defined and tested using the proposed method. Simulation results demonstrate the capability of the algorithm in increasing solar power penetration in the grid up t... [more]
Optimal Coordinated Control of Power Extraction in LES of a Wind Farm with Entrance Effects
Jay P. Goit, Wim Munters, Johan Meyers
October 23, 2018 (v1)
Keywords: adjoints, large eddy simulations, Optimization, turbulent boundary layers, wind farm, wind farm control
We investigate the use of optimal coordinated control techniques in large eddy simulations of wind farm boundary layer interaction with the aim of increasing the total energy extraction in wind farms. The individual wind turbines are considered as flow actuators, and their energy extraction is dynamically regulated in time, so as to optimally influence the flow field. We extend earlier work on wind farm optimal control in the fully-developed regime (Goit and Meyers 2015, J. Fluid Mech. 768, 5⁻50) to a ‘finite’ wind farm case, in which entrance effects play an important role. For the optimal control, a receding horizon framework is employed in which turbine thrust coefficients are optimized in time and per turbine. Optimization is performed with a conjugate gradient method, where gradients of the cost functional are obtained using adjoint large eddy simulations. Overall, the energy extraction is increased 7% by the optimal control. This increase in energy extraction is related to faster... [more]
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]
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]
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