Higher Objective Function Values Research Articles

Design of Steam Alternating Solvent Process Operational Parameters Considering Shale Heterogeneity

Summary The steam alternating solvent (SAS) process involves multiple cycles of steam and solvent (e.g., propane) injected into a horizontal well pair to produce heavy oil. These solvent-based methods entail a smaller environmental footprint with reduced water usage and greenhouse gas emissions. However, the lack of understanding regarding the influences of reservoir heterogeneities, such as shale barriers, remains a significant risk for field-scale predictions. Additionally, the proper design of the process is challenging because of the uncertain heterogeneity distribution and optimization of multiple conflicting objectives. This work develops a novel hybrid multiobjective optimization (MOO) workflow to search a set of Pareto-optimal operational parameters for the SAS process in heterogeneous reservoirs. A set of synthetic homogeneous 2D is constructed using data representative of the Cold Lake reservoir. Next, multiple heterogeneous models (realizations) are built to incorporate complex shale heterogeneities. The resultant set of SAS heterogeneous models is subjected to flow simulation. A detailed sensitivity analysis examines the impacts of shale barriers on SAS production. It is used to formulate a set of operational/decision parameters (i.e., solvent concentration and duration of solvent injection cycles) and the objective functions (cumulative steam/oil ratio and propane retention). The nondominated sorting genetic algorithm II (NSGA-II) is applied to search for the optimal decision parameters. Different formulations of an aggregated objective function, including average, minimum, and maximum, are used to capture the variability in objectives among the multiple realizations of the reservoir model. Finally, several proxy models are included in the hybrid workflow to evaluate the defined objective functions to reduce the computational cost. Results of the optimization workflow reveal that both the solvent concentration and duration of the solvent injection in the early cycles have significant impacts. It is recommended to inject solvent for longer periods during both the early and late SAS stages. It is also noted that cases with higher objective function values are observed with more heterogeneities. This work offers promising potential to derisk solvent-based technologies for heavy oil recovery by facilitating more robust field-scale decision-making.

Enhancement of travel range of electrostatically driven fixed-fixed microbeam in static and dynamic mode using mutated particle swarm optimization

Particle swarm optimization (PSO) is a substantially exercised technique for optimizing structural design problems. One major snag of particle swarm optimization is a possibility of premature convergence, observed in the specific cases where global optimum is encircled by the solutions having higher objective function values. These territories are unexplored and swarm can fall into local optimum. The present work proposes to suggest one of the variants of the PSO algorithm to enhance the reliability. In the proposed algorithm, during each iteration, few particles will be chosen to follow worst solution, this would enable to perform exploration and exploitation of design space simultaneously. Hence, the probability of not getting caught into local optima gets augmented which gives better assurance to the achieved solution. The mutated particle swarm optimization (MPSO) is efficaciously employed for shape optimization of electrostatically driven fixed-fixed microbeams to augment the static and dynamic travel range. The geometry of fixed-fixed microbeams is optimized using continuous, smooth, parametric functions of width and thickness. For structural analysis, Rayleigh–Ritz energy method is utilized to compute the static and dynamic pull-in parameters. Nonlinear design constraints of engineering interest are implemented through penalty approach. Using modified particle swarm optimization, width and thickness profiles of fixed-fixed microbeams that result in maximum pull-in displacement have been obtained. Augmented pull-in parameters for optimized geometry of microbeams have been endorsed using 3-D finite element analysis. The optimization parameters reveal maximum rise in the static pull-in displacement by 41.25% and dynamic pull-in displacement by 39.15% of fixed-fixed microbeam compared to referential prismatic microbeam. Efficacy of mutated particle swarm optimization is emphasized through 18 representative cases. Mutated particle swarm optimization converges in around 20% of time in comparison with the hybrid simulated annealing, without getting trapped into local minimum in all the test cases.

Generative adversarial network guided topology optimization of periodic structures via Subset Simulation

Topology optimization offers great potential to design periodic structures with desired bandgap properties. This paper proposes a novel Subset Simulation (SS) based topology optimization framework by integrating SS with generative adversarial network (GAN). First, the topology optimization problem is reformulated as a rare event simulation problem in reliability analysis, where the optimal solutions are analogously the rare event samples close to failure. Then SS, which has been developed for efficient simulation of rare events in reliability analysis, is used to effectively find the optimal topologies. In each iteration of SS, to address the challenge of simulating samples from high-dimensional design space (stemming from discretization of the unit cell to represent different topologies), this paper proposes to use GANs to learn an implicit model for the underlying high-dimensional failure distribution based on existing failure samples (i.e., topologies with higher objective function values) from the previous iteration in SS, and then use the trained GANs to directly and efficiently generate failure samples (i.e., new promising topologies). Overall, the proposed SS-based and GAN-guided topology optimization algorithm can facilitate efficient topology optimization of periodic structures. The effectiveness and efficiency of the proposed approach are demonstrated through topology optimization of 2D periodic structures.

Parametric Shape Optimization of Pin-Fin Arrays Using a Surrogate Model-Based Bayesian Method

Pin-fin arrays are generally present in trailing-edge regions of gas-turbine blades for internal cooling purposes. An elevated temperature is important to enhance the thermodynamic efficiency of gas-turbine cycles, and internal blade-cooling mechanisms are employed to mitigate material failure at these high temperatures. It is also important to have high heat transfer at a limited pressure drop to ensure that the overall cycle efficiency is not affected by power loss from driving the flow through internal cooling circuits. A pin-fin shape optimization has been carried out, with the array performance efficiency factor as the objective function, using surrogate modeling-based Bayesian methods. Surrogate modeling/machine learning approaches are instrumental in exploring global solutions to optimization problems using an exploration versus exploitation approach. This ensures prudent use of computational resources for computationally expensive computational fluid dynamics simulations. By varying geometric parameters of the pin fins, a three-dimensional design space has been explored to obtain an optimum pin-fin shape that has the maximum efficiency in the developing region of the flow. An improvement in the efficiency parameter of 1.4 times that of the baseline was obtained in a three-row pin-fin case, and the results were compared with those of genetic-algorithm-based optimization (nondominated sorted genetic algorithm–II). Computation time for optimization was saved by 52%, as an optimum was obtained at lower number of iterations when compared to the genetic algorithm approach, with a 1.2% higher objective function value. This shows an improvement in performance of the current novel approach compared to another stochastic method.

Performance Analysis of IoT-Based Health and Environment WSN Deployment.

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

An Intelligent Programmed Genetic Algorithm with advanced deterministic diversity creating operator using objective surface visualization

This paper presents a new fast Intelligent Programmed Genetic Algorithm (IPGA) based evolutionary optimization algorithm which requires lesser number of objective function evaluation for reaching optima. The proposed algorithm, apart from using probabilistic genetic operator, i.e. crossover and mutation, also uses a deterministic diversity creating operator for generating new solution in the current population. This is done by first projecting objective surface from higher dimension to lower dimension for visualization purpose and then deterministically generates new solution using some predefined rules in the region with higher objective function value. As the newly generated solution is in lower-dimensional space, these solutions are again projected back to higher dimensional space and then the objective function is evaluated at that point. The proposed IPGA is tested on three different categories of standard test functions viz. Unimodal function (2 Test Function), Unrotated Multimodal function (6 Test Function) and Rotated Multimodal function (5 Test Function). Simulation results were compared with that obtained using Binary Coded GA, Real Coded GA, recently proposed GA with Differential Evolution crossover operator (GA–DEx) and another success-history-based adaptive GA with aging mechanism (GA–aDExSPS) in terms of mean and standard deviation of the objective function, average number of objective function evaluation required to reach optima and algorithmic complexity. Simulation results clearly demonstrate better performance of the proposed IPGA when compared with other variants of GAs.

Comparison of population-based algorithms for optimizing thinnings and rotation using a process-based growth model

ABSTRACTStand management optimization has long been computationally demanding as increasingly detailed growth and yield models have been developed. Process-based growth models are useful tools for predicting forest dynamics. However, the difficulty of classic optimization algorithms limited its applications in forest planning. This study assessed alternative approaches to optimizing thinning regimes and rotation length using a process-based growth model. We considered (1) population-based algorithms proposed for stand management optimization, including differential evolution (DE), particle swarm optimization (PSO), evolution strategy (ES), and (2) derivative-free search algorithms, including the Nelder–Mead method (NM) and Osyczka’s direct and random search algorithm (DRS). We incorporated population-based algorithms into the simulation-optimization system OptiFor in which the process-based model PipeQual was the simulator. The results showed that DE was the most reliable algorithm among those tested. Meanwhile, DRS was also an effective algorithm for sparse stands with fewer decision variables. PSO resulted in some higher objective function values, however, the computational time of PSO was the longest. In general, of the population-based algorithms, DE is superior to the competing ones. The effectiveness of DE for stand management optimization is promising and manifested.

MO‐G‐137‐04: Using Molecular Dynamics Simulation Algorithm to Overcome the Local Minima of Gradient Methods in IMPT Treatment Plan Optimization

Purpose: The gradient optimization methods such as LBFGS are sensitive to starting conditions and can be trapped in local minima. Molecular dynamic (MD) simulation algorithm, a powerful computational technique used in material science, has the potential to overcome the local minima problem. The purpose of this study is to investigate whether MD algorithm can get out of the local minima, in which LBFGS will be trapped for the optimization problem in intensity modulated proton therapy (IMPT). Methods: A prostate cancer patient was selected for this study. Three different starting conditions was used to optimize the IMPT plans: (a) Forward wedge (FW), all beamlet weights are set to the same weight; (b) SOBP, beamlets weights are arranged to deliver a flat dose on the target; (c) Inverse wedge (IW), the weights are set to distal tracking like resulting very low dose on Femoral Heads. The dose‐based objective function, which only optimizes the uniformity of PTV dose, was used for both LBFGS and MD algorithms. Results: MD converged to the objective function value (OFV) of 3.46 regardless of different initial conditions. LBFGS converged to 3.68, 3.72 and 1082.6 starting with initial conditions FW, SOBP and IW, respectively. For PTV dose coverage, both MD and LBFGS achieved to similar results from FW and SOBP initial conditions. However, using IW initial condition, LBFGS was trapped into a local minimum reflected by a higher OFV and worse PTV coverage (D99=68.5 Gy) while MD converged to a lower OFV and better PTV coverage (D99=75.6 Gy). Conclusion: For the first time, we demonstrated that MD algorithm could be used in radiation therapy planning optimization to overcome the local minima problem which is a major shortcoming of gradient based algorithm widely adopted in radiation therapy optimization problem.

AN INTEGRATED INTERTEMPORAL LINEAR PROGRAMMING MODEL UNDER RISK FOR SHORT-TERM FINANCIAL PLANNING*

LINEAR PROGRAMMING MODELS are frequently used in financial literature as vehicles for determining the firm's theoretical optimal operating plan. Only one integrated linear programming model has been successfully applied to a non-financial business.t Since the value of theory is confirmed through repeated applications, the purpose of this research was to build, test, and evaluate another linear programming model for short-term financial planning. One multi-product firm, a food processor, served as the subject of this study since it complied with inherent assumptions of linear programming models. The objective function of this model was structured to maximize revenue over six monthly time periods. The rates of return for production of real goods were included in the objective function along with external financial rates of return. The integrated intertemporal linear programming model for this firm had 1745 variables and 503 constraints. Before this model was used to calculate an optimal operating plan, however, the model was tested against prior data to verify its duplication of the firm's actual resource consumption and allocation. The model's optimal operating plan had an objective function value which exceeded the firm's actual performance by 33 per cent. The model was structured to have the option of investing funds in operations, repaying debt or purchasing Treasury bills. Consequently, the model's operating plan produced the optimal multi-period schedule for the acquisition and commitment of funds. Dual values, or shadow prices, were analyzed to determine the marginal values of all constrained resources. In particular, the dual value of funds was used as the basis for calculating the marginal revenue product schedule of funds. Shadow prices also were used in conjunction with the range of the objective function to measure the interaction of commodity market returns and financial market returns on the firm. Two methods of quantifying product market price variability, or risk, were incorporated into the model. The first method was to substitute the reciprocal of the coeffi/X cient of variation a for the objective function's revenue coefficients. The second method was to substitute in the revenue coefficients of the model the upper and lower limits of revenue forecasts. Since both methods incorporating risk had a higher objective function value than the firm, if can be concluded that the firm overestimated the effects of risk on the operating plan. This study demonstrated the linear programming models can be used for short-term financial planning, and that the cost of building such models is minimal compared to the direct monetary benefits obtained from using the model. * A dissertation completed at Georgia State University in 1971. t C. W. Young, Linear Programming and Short-Term Financial Planning (Unpublished Ph.D.