Integrated Optimization System Research Articles

Integrated Optimization System Improves Hydraulic Fracturing Operations

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 209127, “Efficiency and Effectiveness—A Fine Balance: An Integrated System To Improve Decisions in Real-Time Hydraulic Fracturing Operations,” by Somnath Mondal, SPE, Ashan Garusinghe, and Sebastian Ziman, Shell, et al. The paper has not been peer reviewed. _ In the complete paper, the authors demonstrate the need to balance optimizing fracture efficiency with effectiveness; present an integrated system for fracturing optimization using real-time, historical data along with organizational knowledge; and discuss the challenges and key considerations of setting up such a system, along with examples of large, untapped potential that can be unlocked with data science. Introduction Currently, most third-party fracture-monitoring solutions do not provide a true fracture-optimization platform that goes beyond fracture monitoring and efficiency or cost analytics. Without consideration for creating effective fracture geometries and good stimulation distribution, this may lead to poor resource recovery. Integrated fiber-optic diagnostics data have also shown that perforation-cluster screenout signatures sometimes can be observed in the surface-treatment data. Historical analysis of surface-treatment data has revealed that perforation-cluster screenout signatures are widely observed in other basins (Fig. 1). Cluster screenouts lead to severe nonuniformity in stimulation distribution. These examples demonstrate the need for including effectiveness considerations in any real-time fracture-optimization algorithm. The Completions Automation and Remote Technology (CART) Center was set up in 2017 by the operator to monitor and optimize all shale-fracturing operations remotely across multiple basins. The Center is staffed by consultants whose main role is to provide input directly to the engineer on site to optimize each treatment. Currently, this remote model relies on the experience of the consultant to know when and where to provide the right input. Data Preprocessing Modularity is a key component of the authors’ framework. The first step is to process new data, calculating important statistics used in historical analysis. Calculations to generate statistics used within the models also are performed. Models can be applied sequentially within this framework, posing no problems with regard to interdependency. As data are fed into the framework, automated analyses are performed to detect the start and end of a stage in real time. Instead of opting for a complex deep-learning algorithm, the authors implemented a simpler rule-based model to improve operational efficiency of the tool. At each new time stamp, the injection rate and pressure are evaluated to determine if fracturing operations have commenced; should the flow rate fall back to near zero, the framework will reset itself if this was simply a testing period of the stage or counts the stage as ended if the total sand pumped is more than the minimum required for the stage. Easy or Hard Stage A fracturing stage can be classified as “easy” or “hard” depending on how easily injectivity can be achieved and maintained. Engineers try to classify a stage as early as possible to decide how readily a stage will accept sand. The complete paper presents a work flow to assist engineers in the early classification of these stages.

Balancing environmental impacts and economic benefits of agriculture under the climate change through an integrated optimization system

The present study proposes a framework to mitigate impact of climate change on the rice production by maximizing the yield while the energy use and ecological impacts on the river ecosystem as the irrigation source are mitigated. Coupled general circulation model- soil and water assessment tool (SWAT) was utilized to project the impact of climate change on the stream flow. Fuzzy physical habitat simulation was applied to develop the ecological impact function of the river. Moreover, a data-driven model was developed to predict the rice yield through changing water and energy consumption. Finally, all the simulations were utilized in the structure of the optimization model in which minimizing loss of the production, greenhouse gas emission by reducing energy use and physical habitat loss were considered as the objectives. Based on the results, the Nash–Sutcliffe model efficiency coefficient of the SWAT is 0.7 that demonstrates its reliability for simulating the impact of climate change on river flow. The optimization model is able to reduce the impact of climate change on yield of production by balancing water and energy use. In the most pessimistic scenario, water use should approximately be reduced 25% for protecting river ecosystem. However, the optimization model approximately increased energy use 16% for preserving the yield of the rice. Conversely, model decreased the energy use 40% compared with the current condition due to increasing water supply. Moreover, physical habitat loss is less than 50% that means the combined optimization model is able to protect river habitats properly.

Bioenergy Potential of Crop Residues in the Senegal River Basin: A Cropland–Energy–Water-Environment Nexus Approach

Access to energy services is a priority for sustainable economic development, especially in rural areas, where small- and medium-sized enterprises have many difficulties in accessing reliable and affordable electricity. Western African countries are highly dependent on biomass resources; therefore, understanding the potential of bioenergy from crop residues is crucial to designing effective land-management practices. The assessment of the capability to use crop residues for electricity production is particularly important in those regions where agriculture is the dominant productive sector and where electrification through grid extension might be challenging. The objective of this work was to guide the development of sustainable strategies for rural areas that support energy development by simultaneously favouring food self-sufficiency capacity and environmental benefits. These complex interlinkages have been jointly assessed in the Senegal river basin by an integrated optimization system using a cropland–energy–water-environment nexus approach. The use of the nexus approach, which integrates various environmental factors, is instrumental to identify optimal land-energy strategies and provide decision makers with greater knowledge of the potential multiple benefits while minimizing trade-offs of the new solutions such as those connected to farmers’ needs, local energy demand, and food and land aspects. By a context-specific analysis, we estimated that, in 2016, 7 million tons of crop residues were generated, resulting in an electricity potential of 4.4 million MWh/year. Several sustainable land-energy management strategies were explored and compared with the current management strategy. Our results indicate that bioenergy production from crop residues can increase with significant variability from 5% to +50% depending on the strategy constraints considered. An example analysis of alternative irrigation in the Guinea region clearly illustrates the existing conflict between water, energy, and food: strategies optimizing bioenergy achieved increases both for energy and food production (+6%) but at the expense of increasing water demand by a factor of nine. The same water demand increase can be used to boost food production (+10%) if a modest decrease in bioenergy production is accepted (−13%).

A machine-learning-based method for thermal design optimization of residential buildings in highly urbanized areas of Turkey

In 2008, the Turkish government legislated a new regulation mandating the retrofit of existing buildings and construction of energy-efficient buildings. Owing to the requirements of the legislated regulations and environmental concerns, constructing energy-efficient buildings has become a necessity. Researchers working in this field have proposed various optimization techniques to determine energy-optimal design solutions. However, the proposed techniques require a large number of simulations thereby reducing the practicality and inhibiting the wider diffusion of the technique into the industry. Recent studies have demonstrated that the use of surrogate models as alternatives to commercial energy simulation tools significantly reduces the time required for optimization, and a large number of buildings can be optimized in a shorter period. In this study, we aimed at developing a surrogate model-based integrated optimization system to obtain energy-optimal thermal designs for residential buildings in the most urbanized cities in Turkey under different levels of budget constraints. For this purpose, an integrated system consisting of a genetic algorithm optimization technique and gradient boosting machine based surrogate model was developed. The results indicate that the proposed surrogate model-based optimization system is an effective method of overcoming the difficulties of simulation-based optimization methods and it can be integrated into the daily workflow of designers. This study reveals the performance of the scarcely used gradient boosting machine based surrogate model in predicting the thermal loads of buildings. The outcomes of this research will guide designers in finding energy-optimal design alternatives for residential buildings.

Improved Dynamical Particle Swarm Optimization Method for Structural Dynamics

A methodology to the multiobjective structural design of buildings based on an improved particle swarm optimization algorithm is presented, which has proved to be very efficient and robust in nonlinear problems and when the optimization objectives are in conflict. In particular, the behaviour of the particle swarm optimization (PSO) classical algorithm is improved by dynamically adding autoadaptive mechanisms that enhance the exploration/exploitation trade-off and diversity of the proposed algorithm, avoiding getting trapped in local minima. A novel integrated optimization system was developed, called DI-PSO, to solve this problem which is able to control and even improve the structural behaviour under seismic excitations. In order to demonstrate the effectiveness of the proposed approach, the methodology is tested against some benchmark problems. Then a 3-story-building model is optimized under different objective cases, concluding that the improved multiobjective optimization methodology using DI-PSO is more efficient as compared with those designs obtained using single optimization.

An Integrated Optimization System for Safe Job Assignment Based on Human Factors and Behavior

Industrial safety has been deeply improved in the past years, thanks to increasingly sophisticated technologies. Nevertheless, 2.3 million people yearly die worldwide due to occupational illnesses and accidents at work. Human factors can be profitably used for safety improvement because of their influence on the workers’ behavior. This paper presents an integrated optimization system to help companies assign each task to the most suitable worker, minimizing cost, while maximizing expertise and safety. The system is made of three modules. A neural module computes each worker's caution for every task on the basis of some human factors and the worker's behavior. To solve the multiobjective job assignment problem, an evolutionary module approximates the Pareto front through the nondominated sorting genetic algorithm II. Pareto-optimal solutions then form the alternatives of a multicriteria decision-making problem, and the best is selected by a decision module jointly based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution. Validation was carried out involving two footwear companies, where personnel was recruited and reassigned to tasks, respectively. Comparing the worker-task assignment proposed by the system to the one suggested/used by the management, noteworthy low-cost improvement in safety is shown in both scenarios, with low or no decrease in expertise. The proposed system can, thus, contribute to get safer workplaces where risks are less likely and/or less harmful.

Airfoil optimization to improve power performance of a high-solidity vertical axis wind turbine at a moderate tip speed ratio

The relatively low power coefficient restricts the wide application of vertical axis wind turbines (VAWTs). An effective solution to this problem is to design specific airfoil profiles which directly influence the capture ratio of wind power. The main aim of the present study is to develop an automatic airfoil profile optimization system to improve the power performance of a VAWT. A three-bladed high-solidity VAWT is adopted as the research object with its chord length, blade span and rotor diameter being 0.2 m, 0.8 m and 0.8 m, respectively. The optimization is conducted at a moderate tip speed ratio (TSR) with a value of 1.0 and the method of coupled CFD simulations with genetic algorithms is employed. The following points make this paper different from previous studies: (a) introducing Multi-Island Genetic Algorithm to optimize airfoils for VAWTs; (b) investigating the airfoil as part of the VAWT rather than as a single isolated body with 3D simulations. The results show that the power coefficient of the VAWT equipped with the optimized blades sensibly improves at all TSRs from 0.4 to 1.5 and the maximum growth rate of it occurs at TSR = 0.9 with a value of 26.82%. The integrated optimization system used in this paper provides an effective way to generate suitable airfoil profiles for given VAWTs with the goal to achieve higher power efficiency.

A Novel Integrated Optimization System for Earthwork Tasks

Earthworks are part of the construction of any type of ground transport infrastructure. In many road and railway infrastructures earthworks represent up to 30 to 50% of total cost of the construction. Moreover, earthworks involve the use of heavy mechanical equipment (e.g., excavators, dumper trucks, bulldozers and rollers) and repetitive activities that are responsible for large amounts of carbon emissions with negative impact to the environment.In this context, the optimization of earthworks construction activities is becoming increasingly important in recent years, while effective and practical integrated solutions have not been established so far. As such, this work introduces a novel optimization integrated system for earthwork tasks. In this integrated system, the optimization is carried out on various fronts, namely minimization of execution cost and duration, while attempting to reduce environmental impacts, such as carbon emissions. In order to achieve this, the integration of a wide array of technologies is required, so as to allow for a proper adjustment to reality. These range from evolutionary computation and data mining (i.e., soft computing), to geographic information systems and linear programming. The former are used firstly to provide realistic estimates of the productivity of available resources (i.e., equipment), and secondly to perform their optimal allocation throughout the construction site. Concurrently, the latter are employed for supporting the optimization of resource and material management, as well as of the trajectories associated with transportation of material from excavation to embankment fronts.The system has been validated using real-world data stemming from a Portuguese road construction site. Results show that the proposed system is very competitive when compared with the manual allocation methodologies currently used for the design and construction of earthworks. In fact, the system can output several different resource distribution solutions, which comprehend a trade-off between the referred optimization objectives, enhancing the flexibility of design by allowing the user to select the solution that best fits the project restrictions (e.g., deadline, budget). As such, the system is capable of allocating the available equipment in a way that maximizes its potential and productivity, while indirectly guaranteeing minimum carbon emissions in each possible solution. These results emphasize the importance of using this kind of decision support/optimization tools in the design and construction of earthworks.

An integrated parameter optimization system for MIMO plastic injection molding using soft computing

This study proposes an integrated optimization system to find out the optimal parameter settings of multi-input multi-output (MIMO) plastic injection molding (PIM) process. The system is divided into two stages. In the first stage, the Taguchi method and analysis of variance (ANOVA) are employed to perform the experimental work, calculate the signal-to-noise (S/N) ratio, and determine the initial process parameters. The back-propagation neural network (BPNN) is employed to construct an S/N ratio predictor and a quality predictor. The S/N ratio predictor and genetic algorithms (GA) are integrated to search for the first optimal parameter combination. The purpose of this stage is to reduce the process variance. In the second stage, the quality predictor is combined with particle swarm optimization (PSO) to find the final optimal parameters. The quality characteristics, product length and warpage, are dedicated to finding the optimal process parameters. After the numerical analysis, the optimal parameters can meet the lowest variance and the product quality requirements simultaneously. Experimental results show that the proposed optimization system can not only satisfy the quality specification but also improve stability of the PIM process.

An intelligent integrated optimization system for the proportioning of iron ore in a sintering process

The proportioning of iron ore is the first step of the sintering process. It mixes different kinds of iron ores with coke, limestone, dolomite, and returned sinter to produce a raw mix for the production of qualified sinter. The chemical components and proportions of the raw materials determine the chemical and physical characteristics of the resulting sinter, and thus the quality of the sinter and the amount of SO2 emissions. The prices of the raw materials and their proportions determine the price of the sinter. In this study, an intelligent integrated optimization system (IIOS) was developed for the proportioning step, which contains two phases: the first and second proportionings. First, the sintering process was analyzed, and the requirements of the proportioning step were specified. Next, an IIOS with two levels (intelligent integrated optimization, basic automation) was built. In the intelligent integrated optimization level, an intelligent integrated optimizer (IIO) produces an optimal dosing scheme. The IIO has three parts: a cascade integrated quality-prediction model, the optimization of the first proportioning, and the optimization of the second proportioning. Computational intelligence methods predict the quality of sinter. Then, the predicted quality indices are fed back to the optimizations of the first and second proportionings to find feasible optimal dosing schemes. The IIOS was implemented in an iron and steel plant. Actual runs show that the system reduced production costs by 43.014CNY/t and SO2 emissions by 0.001% on average.

Meshing and integrated optimisation for free-form reticulated shells

Presented herein is a study on the meshing and the integrated optimisation technique combined with configuration and mechanical behaviour for free-form reticulated shells. It focuses on the realisation of the practical layouts of meshes and improves the evaluation criteria for grid quality. For the preparation for the integrated optimisation, the bicubic quasi-uniform B-spline surface is applied to describe the free-form surface, and the energy method is introduced to smooth free-form reticulated shell through fairing factors. In order to perform optimisation automatically, a novel integrated optimisation system is established including fairing, finite element analysis (FEA) and optimisation programme modules. Based on the relationship between the fairing factors and the structural configuration, the zoned fairing factors are chosen as design variables (DVs). The FEA module is introduced to analyse and calculate the total structural strain energy of the structural components. By using the optimisation mod...

Application of Intelligent Integrated Optimization System for Raw Material Proportioning in Lead–Zinc Sintering Blending Process

The blending process as the first working procedure in lead–zinc sintering processes (LZSP) is a key step to guarantee the quality of products in the lead–zinc sintering production. This paper pres...

Learning-based ship design optimization approach

With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the optimization process and adjust the complex design environment, ship designers usually rely on their personal experience to assist the design work. However, traditional experience, which largely depends on the designer’s personal skills, often makes the design quality very sensitive to the experience and decreases the robustness of the final design. This paper proposes a new machine-learning-based ship design optimization approach, which uses machine learning as an effective tool to give direction to optimization and improves the adaptability of optimization to the dynamic design environment. The natural human learning process is introduced into the optimization procedure to improve the efficiency of the algorithm. Q-learning, as an approach of reinforcement learning, is utilized to realize the learning function in the optimization process. The multi-objective particle swarm optimization method, multi-agent system, and CAE software are used to build an integrated optimization system. A bulk carrier structural design optimization was performed as a case study to evaluate the suitability of this method for real-world application.

Integrated Optimization by Multi‐Objective Particle Swarm Optimization

AbstractIn this letter, integrated optimization system, a new framework of practical optimization, is expanded to multi‐objective optimization problem. This system is used in order to reduce the number of accesses to a simulator. On the basis of simulation results using some typical benchmark problems, it is shown that the proposed integrated optimization system enables to obtain relatively good Pareto solutions with drastic reduction in the number of function calls for evaluating the performance index values of systems. Copyright © 2010 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Integrated optimization system for high pressure die casting processes

High pressure die casting (HPDC) is a versatile process for producing engineered metal parts by forcing molten metal under high pressure into reusable steel dies. However there are a large number of attributes involved which contribute to the complexity of the process. A novel integrated approach is developed to optimize the high pressure die casting processes. The die temperature profiles will be studied with infrared thermograph technology and the internal cooling system will be optimized to provide even cooling to the components and the die. The heat stored in the die and the components is studied with image processing. Based on the geometrical profile of the components, cooling channels can be redesigned to improve the cooling efficiency while the cooling time is reduced. This will not only significantly improve the quality of the castings but also improve the productivity of the process.

Two-Level Integrated Optimization System for Planning of Emergency Evacuation

This paper presents a two-level integrated optimization system for use in generating the candidate set of optimal evacuation plans that serve as the input for simulation-based evacuation systems. In the proposed system, the high-level optimization aims to maximize the throughput during the specified evacuation duration, and the low-level optimization is intended to minimize the total travel time as well as the waiting time for the entire operation if the specified duration is sufficient for meeting all evacuation demands. To effectively represent traffic flow relations with mathematical formulations, this paper employs the cell transmission concept, but with a revised formulation for large-scale network applications. The performance of the proposed models and their applicability has been tested with a microscopic simulation program that replicates the Ocean City evacuation network. Evaluation results from these numerical studies have demonstrated the promising properties of the proposed integrated optimization system.

An Interval-Parameter Fuzzy Approach for Multiobjective Linear Programming Under Uncertainty

An interval-parameter fuzzy linear programming method (IFMOLP) is proposed in this study for multiple objective decision-making under uncertainty. As a hybrid of interval-parameter and fuzzy methodologies, the IFMOLP incorporates interval-parameter linear programming and fuzzy multiobjective programming approaches to form an integrated optimization system. The method inherits advantages of interval-parameter programming, and allows uncertainties and decision-makers’ aspirations to be effectively communicated into its programming processes and resulting solutions. Membership functions for both objectives and constraints are formulated to reflect uncertainties in different system components and their interrelationships. An interactive solution procedure has been developed based on solution approaches of the interval-parameter and fuzzy programming techniques, plus necessary measures for handling the multiobjective feature. A didactic example is provided in the paper to illustrate the detailed solution process. Possibilities of further improvements by seeking Pareto optimum and incorporating flexible preference within constraints are also discussed.

Dynamic portfolio management under competing representations

PurposeTo solve the multi‐period portfolio management problem under transactions costs.Design/methodology/approachWe apply a recently designed super genetic hybrid algorithm (SuperGHA) – an integrated optimisation system for simultaneous parametric search and non‐linear optimisation – to a recursive portfolio management decision support system (SHAREX). The parametric search machine is implemented as a genetic superstructure, producing tentative parameter vectors that control the ultimate optimisation process.FindingsSHAREX seems to outperform the buy and hold‐strategy on the Finnish stock market. The potential of a technical portfolio system is best exploitable under favorable market conditions.Originality/valueA number of robust engines for matrix algebra, mathematical programming and numerical calculus have been integrated with SuperGHA. The engines expand its scope as a general‐purpose algorithm for mathematical programming.

Multiobjective Optimization Using Coupled Response Surface Model and Evolutionary Algorithm.

In this work we develop an efficient approach for computationally expensive multiobjective design optimization problems. In this approach we bring together design of experiment, a response surface model, a genetic algorithm, and computational-fluid-dynamics analysis tools to provide an integrated optimization system. We use an improved hypercube sampling to preselect an array of design points on which the computational-fluid-dynamics code will run. Then a computationally cheap surrogate model is constructed based on response surface approximation. A real-coded genetic algorithm is then applied on the surrogate model to perform multiobjective optimization. Representative solutions are chosen from the Pareto-optimal front to verify against the computational-fluid-dynamics code. This proposed method is used in the redesign of a single-stage turbopump, a two-stage turbopump, and the NASA rotor67 transonic compressor blade

Approximate Optimization Using RBF Network and Genetic Range Genetic Algorithms (Proposal of Base Function and Basic Consideration)

In this paper, we will propose integrated optimization system that is composed by RBF network as approximation tool and Genetic Range Genetic Algorithms as optimizer. This system has function of optimizing radius of basis function in optimization, indication of next searching points by using basis function and optimization by using Genetic Range Genetic Algorithms that is a step further of Adaptive Range Genetic Algorithms. Key success in approximation by using RBF network lies in giving good data sets. Here, “good data sets” means that they can update accuracy around local optimum points, and also they can give global information of original function's response surface. In that sense, indication function is quite important in this system. For that purpose, we introduce base function which is composed by approximation RBF network. We demonstrated the proposed system to a bench mark problem to show efficiency of the proposed method. In constraint optimization case, it works something like active set strategy, and we can reduce the number of function calls.