Subjects
Records with Subject: Optimization
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
焦炉煤气除硫以及联合循环发电的优化
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]
Multistage Stochastic Programming Models for Pharmaceutical Clinical Trial Planning
Zuo Zeng, Selen Cremaschi
July 31, 2018 (v1)
Subject: Optimization
Keywords: clinical trial planning, endogenous uncertainty, mixed-integer programming, multistage stochastic programming, optimization under uncertainty
Clinical trial planning of candidate drugs is an important task for pharmaceutical companies. In this paper, we propose two new multistage stochastic programming formulations (CM1 and CM2) to determine the optimal clinical trial plan under uncertainty. Decisions of a clinical trial plan include which clinical trials to start and their start times. Its objective is to maximize expected net present value of the entire clinical trial plan. Outcome of a clinical trial is uncertain, i.e., whether a potential drug successfully completes a clinical trial is not known until the clinical trial is completed. This uncertainty is modeled using an endogenous uncertain parameter in CM1 and CM2. The main difference between CM1 and CM2 is an additional binary variable, which tracks both start and end time points of clinical trials in CM2. We compare the sizes and solution times of CM1 and CM2 with each other and with a previously developed formulation (CM3) using different instances of clinical trial... [more]
Multi-Objective Optimization of Experiments Using Curvature and Fisher Information Matrix
Erica Manesso, Srinath Sridharan, Rudiyanto Gunawan
July 31, 2018 (v1)
Subject: Optimization
Keywords: biological processes, curvature, design of experiments, Fisher information matrix, mathematical modeling, multi-objective optimization
The bottleneck in creating dynamic models of biological networks and processes often lies in estimating unknown kinetic model parameters from experimental data. In this regard, experimental conditions have a strong influence on parameter identifiability and should therefore be optimized to give the maximum information for parameter estimation. Existing model-based design of experiment (MBDOE) methods commonly rely on the Fisher information matrix (FIM) for defining a metric of data informativeness. When the model behavior is highly nonlinear, FIM-based criteria may lead to suboptimal designs, as the FIM only accounts for the linear variation in the model outputs with respect to the parameters. In this work, we developed a multi-objective optimization (MOO) MBDOE, for which the model nonlinearity was taken into consideration through the use of curvature. The proposed MOO MBDOE involved maximizing data informativeness using a FIM-based metric and at the same time minimizing the model cur... [more]
Optimization through Response Surface Methodology of a Reactor Producing Methanol by the Hydrogenation of Carbon Dioxide
Grazia Leonzio
July 31, 2018 (v1)
Subject: Optimization
Keywords: ANOVA analysis, carbon capture and utilization, methanol production, Optimization, process simulation, response surface methodology
Carbon dioxide conversion and utilization is gaining significant attention worldwide, not only because carbon dioxide has an impact on global climate change, but also because it provides a source for potential fuels and chemicals. Methanol is an important fuel that can be obtained by the hydrogenation of carbon dioxide. In this research, the modeling of a reactor to produce methanol using carbon dioxide and hydrogen is carried out by way of an ANOVA and a central composite design. Reaction temperature, reaction pressure, H₂/CO₂ ratio, and recycling are the chosen factors, while the methanol production and the reactor volume are the studied responses. Results show that the interaction AC is common between the two responses and allows improvement of the productivity in reducing the volume. A mathematical model for methanol production and reactor volume is obtained with significant factors. A central composite design is used to optimize the process. Results show that a higher productivity... [more]
Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms
Jackson Udy, Brigham Hansen, Sage Maddux, Donald Petersen, Spencer Heilner, Kevin Stevens, David Lignell, John D. Hedengren
July 31, 2018 (v1)
Subject: Optimization
Keywords: EOR, history matching, recovery optimization, waterflooding, well placement
This paper presents a review of history matching and oil field development optimization techniques with a focus on optimization algorithms. History matching algorithms are reviewed as a precursor to production optimization algorithms. Techniques for history matching and production optimization are reviewed including global and local methods. Well placement, well control, and combined well placement-control optimization using both secondary and tertiary oil production techniques are considered. Secondary and tertiary recovery techniques are commonly referred to as waterflooding and enhanced oil recovery (EOR), respectively. Benchmark models for comparison of methods are summarized while other applications of methods are discussed throughout. No single optimization method is found to be universally superior. Key areas of future work are combining optimization methods and integrating multiple optimization processes. Current challenges and future research opportunities for improved model v... [more]
Special Issue “Real-Time Optimization” of Processes
Dominique Bonvin
July 31, 2018 (v1)
Subject: Optimization
Process optimization is the method of choice for improving the performance of industrial processes, while also enforcing the satisfaction of safety and quality constraints.[...]
An Analysis of the Directional-Modifier Adaptation Algorithm Based on Optimal Experimental Design
Sébastien Gros
July 31, 2018 (v1)
Subject: Optimization
Keywords: directional-modifier adaptation, experimental design, modifier approach, optimality loss function
The modifier approach has been extensively explored and offers a theoretically-sound and practically-useful method to deploy real-time optimization. The recent directional-modifier adaptation algorithm offers a heuristic to tackle the modifier approach. The directional-modifier adaptation algorithm, supported by strong theoretical properties and the ease of deployment in practice, proposes a meaningful compromise between process optimality and quickly improving the quality of the estimation of the gradient of the process cost function. This paper proposes a novel view of the directional-modifier adaptation algorithm, as an approximation of the optimal trade-off between the underlying experimental design problem and the process optimization problem. It moreover suggests a minor modification in the tuning of the algorithm, so as to make it a more genuine approximation.
Modifier Adaptation for Real-Time Optimization—Methods and Applications
Alejandro G. Marchetti, Grégory François, Timm Faulwasser, Dominique Bonvin
July 30, 2018 (v1)
Subject: Optimization
Keywords: modifier adaptation, plant-model mismatch, real-time optimization
This paper presents an overview of the recent developments of modifier-adaptation schemes for real-time optimization of uncertain processes. These schemes have the ability to reach plant optimality upon convergence despite the presence of structural plant-model mismatch. Modifier Adaptation has its origins in the technique of Integrated System Optimization and Parameter Estimation, but differs in the definition of the modifiers and in the fact that no parameter estimation is required. This paper reviews the fundamentals of Modifier Adaptation and provides an overview of several variants and extensions. Furthermore, the paper discusses different methods for estimating the required gradients (or modifiers) from noisy measurements. We also give an overview of the application studies available in the literature. Finally, the paper briefly discusses open issues so as to promote future research in this area.
Real-Time Optimization under Uncertainty Applied to a Gas Lifted Well Network
Dinesh Krishnamoorthy, Bjarne Foss, Sigurd Skogestad
July 30, 2018 (v1)
Subject: Optimization
Keywords: gas lift optimization, real-time optimization (RTO), scenario tree, uncertainty, worst case optimization
In this work, we consider the problem of daily production optimization in the upstream oil and gas domain. The objective is to find the optimal decision variables that utilize the production systems efficiently and maximize the revenue. Typically, mathematical models are used to find the optimal operation in such processes. However, such prediction models are subject to uncertainty that has been often overlooked, and the optimal solution based on nominal models can thus render the solution useless and may lead to infeasibility when implemented. To ensure robust feasibility, worst case optimization may be employed; however, the solution may be rather conservative. Alternatively, we propose the use of scenario-based optimization to reduce the conservativeness. The results of the nominal, worst case and scenario-based optimization are compared and discussed.
Online Optimization Applied to a Shockless Explosion Combustor
Jan-Simon Schäpel, Thoralf G. Reichel, Rupert Klein, Christian Oliver Paschereit, Rudibert King
July 30, 2018 (v1)
Subject: Optimization
Keywords: constant volume combustion, extremum seeking control, shockless explosion combustion
Changing the combustion process of a gas turbine from a constant-pressure to a pressure-increasing approximate constant-volume combustion (aCVC) is one of the most promising ways to increase the efficiency of turbines in the future. In this paper, a newly proposed method to achieve such an aCVC is considered. The so-called shockless explosion combustion (SEC) uses auto-ignition and a fuel stratification to achieve a spatially homogeneous ignition. The homogeneity of the ignition can be adjusted by the mixing of fuel and air. A proper filling profile, however, also depends on changing parameters, such as temperature, that cannot be measured in detail due to the harsh conditions inside the combustion tube. Therefore, a closed-loop control is required to obtain an adequate injection profile and to reject such unknown disturbances. For this, an optimization problem is set up and a novel formulation of a discrete extremum seeking controller is presented. By approximating the cost function w... [more]
A Continuous Formulation for Logical Decisions in Differential Algebraic Systems using Mathematical Programs with Complementarity Constraints
Kody M. Powell, Ammon N. Eaton, John D. Hedengren, Thomas F. Edgar
July 30, 2018 (v1)
Subject: Optimization
Keywords: complementarity constraints, differential algebraic equations, dynamic optimization, orthogonal collocation
This work presents a methodology to represent logical decisions in differential algebraic equation simulation and constrained optimization problems using a set of continuous algebraic equations. The formulations may be used when state variables trigger a change in process dynamics, and introduces a pseudo-binary decision variable, which is continuous, but should only have valid solutions at values of either zero or one within a finite time horizon. This formulation enables dynamic optimization problems with logical disjunctions to be solved by simultaneous solution methods without using methods such as mixed integer programming. Several case studies are given to illustrate the value of this methodology including nonlinear model predictive control of a chemical reactor using a surge tank with overflow to buffer disturbances in feed flow rate. Although this work contains novel methodologies for solving dynamic algebraic equation (DAE) constrained problems where the system may experience... [more]
Global Deterministic Optimization with Artificial Neural Networks Embedded
Global deterministische Optimierung von Optimierungsproblemen mit künstlichen neuronalen Netzwerken
Artur M Schweidtmann, Alexander Mitsos
July 4, 2018 (v1)
Subject: Optimization
Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for deterministic global optimization of ANN embedded optimization problems. The proposed method is based on relaxations of algorithms using McCormick relaxations in a reduced-space [\textit{SIOPT}, 20 (2009), pp. 573-601] including the convex and concave envelopes of the nonlinear activation function of ANNs. The optimization problem is solved using our in-house global deterministic solver MAiNGO. The performance of the proposed method is shown in four optimization examples: an illustrative function, a fermentation process, a compressor plant and a chemical process optimization. The results show that computational solution time is favorable compared to the global general-purpose optimization solver BARON.
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