Optimization Solution Research Articles

Surrogate-based optimisation of process systems to recover resources from wastewater

Wastewater systems are transitioning towards integrative process systems to recover multiple resources whilst simultaneously satisfying regulations on final effluent quality. This work contributes to the literature by bringing a systems-thinking approach to resource recovery from wastewater, harnessing surrogate modelling and mathematical optimisation techniques to highlight holistic process systems. A surrogate-based process synthesis methodology was presented to harness high-fidelity data from black box process simulations, embedding first principles models, within a superstructure optimisation framework. Modelling tools were developed to facilitate tailored derivative-free optimisation solutions widely applicable to black box optimisation problems. The optimisation of a process system to recover energy and nutrients from a brewery wastewater reveals significant scope to reduce the environmental impacts of food and beverage production systems. Additionally, the application demonstrates the capabilities of the modelling methodology to highlight optimal processes to recover carbon, nitrogen, and phosphorous resources whilst also accounting for uncertainties inherent to wastewater systems.

Multi-objective optimization of regional integrated energy system matrix modeling considering exergy analysis and user satisfaction

Integrated energy system achieves the collaborative optimization of different energy forms such as electricity/gas/heat/cold, and it is an important strategic development direction in the international energy field in the future. However, with the development of smart grid technology and the increase of renewable energy penetration, it needs more flexible load to adapt to the changing energy supply–demand relationship. Considering the impact of user-side load fluctuations on the system, this paper establishes a two-tier closed-loop system model formed by users and the market. The upper-layer is based on the user’s perspective. A comprehensive user satisfaction model is established by the radar chart area method and consumer preference theory. The lower-layer is based on the market perspective. It proposes an improved layering matrix Energy Hub model and conducts the regional integrated energy system exergy efficiency evaluation study. Then, taking economy, carbon emission and exergy efficiency as objective functions, a multi-objective optimization model is constructed. Non-dominated Sorting Genetic Algorithms-II is applied to solve the model, and the output parameters is a Pareto front surface. A combination weighting TOPSIS method is used to select a compromise solution of multi-objective optimization from the optimal solution set. Compared with the primary energy utilization efficiency as the objective function, the exergy efficiency reduces the system cost by 8.64%. Finally, the multi-objective optimization solution based on different user satisfaction is obtained. Through the analysis of the examples, compared with the original load, the introduction of the upper-layer user satisfaction model reduces the system cost by 2.8%. The reasonable setting of user satisfaction and load adjustment amount can improve the economy of system, which verifies the effectiveness of the two-tier closed-loop system based on user satisfaction model.

Study on the subsurface characteristics of textured surfaces under EHL condition

PurposeThe purpose of this study is to investigate the influence of surface texture on the subsurface characteristics of contact interfaces under elastohydrodynamic lubrication condition. As a typical contact form of gears and bearings, the optimization of friction characteristics at the elastohydrodynamic lubrication (EHL) interface has attracted the attention of scholars. Laser surface texturing is a feasible optimization solution, but there have been concerns about whether the surface texture of high-pair parts will affect their fatigue life.Design/methodology/approachTo examine the impact of texture preparation on the subsurface characteristics of high-pair interfaces under EHL conditions, a point contact EHL model is developed that takes into account the effect of textured surface topography. The pressure and thickness of the oil film are calculated as input parameters under different loads and entrainment velocities. The finite element method is used to simulate the impact of textures with varying diameters, densities and depths on the subsurface characteristics of the elastohydrodynamic interface. According to ISO 25178, analyze the relationship between 3D topography parameters and subsurface characteristics and study the trend of friction characteristics and subsurface characteristics based on the results of the ball on disc friction tests.FindingsThe outcomes suggest that under different rotational velocity and load conditions, the textured surfaces exhibit improved friction reduction effects; however, the creation of textures can result in significant subsurface plastic deformation and local peeling. The existence of texture makes the larger stress zone in the subsurface layer closer to the surface, leading to fatigue failure near the surface. Reasonable design parameters can help enhance the attributes of the subsurface. A smaller Sa and a Str greater than 0.5 can achieve ideal subsurface properties on the textured surface.Originality/valueThis paper investigates the influence of surface texture on the friction and subsurface characteristics of EHL interfaces and analyzes the impact of surface texture on interface contact performance while achieving lubrication improvement functional characteristics. The results provide theoretical support for the optimization design and functional regulation of surface texture in EHL interfaces.Peer reviewThe peer review history for this article is https://publons.com/publon/10.1108/ILT-10-2023-0324/

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning

Machine learning is an effective technique for optimizing real-time electronics product data analysis. It can efficiently handle large electronics product datasets, reducing processing time and resource requirements for generating insights. This study assesses the current status of methods and applications for optimizing real-time data analysis by examining existing research in machine learning-based recommendation systems for electronic products. The indicated subjects encompass using machine learning algorithms to discern characteristics and correlations from large datasets, applying machine learning for prognostic analytics and projection, and utilizing machine learning to identify anomalies. The paper provides examples of machine learning-based evaluation optimization solutions that focus on utilizing unorganized data and delivering real-time dashboards. Presented here is a discussion on the complex challenges and potential benefits associated with utilizing machine learning to optimize real-time data processing. Machine learning may efficiently expedite real-time data assessment while delivering precise and timely outcomes

Enhanced Network Reliability Following Emergency (E911) Calls

Purpose: In this research, the purpose is to explore E911 call reliability requirements, study real-world issues related to telecommunication networks transitioning from LTE to 5G (NR) and WCDMA, and present network optimization solutions. The primary objective is to ensure the continuous supply of emergency services and improve the dependability of Enhanced 911 (E911) calls.
 Methodology: The research methodology involves an examination of the transition from LTE to 5G (NR) and WCDMA in telecommunication networks. The study delves into government-mandated E911 call reliability requirements and conducts a detailed analysis of two real-world issues affecting tight connectivity for E911 calls. Additionally, the research proposes network optimization solutions to address these challenges and enhance the overall reliability of emergency services.
 Findings: The findings of this research reveal insights into government-mandated E911 call reliability requirements and identify two practical issues affecting the continuity of emergency services during the transition from LTE to 5G (NR) and WCDMA.
 Unique contributor to theory, policy and practice: The study presents network optimization solutions aimed at overcoming these challenges, with the ultimate goal of improving the dependability of E911 calls and enhancing public safety.

Impacts of spatial imputation on location-allocation problem solutions

Georeferenced data often contain missing values, and such missing values can considerably affect spatial modeling. A spatial location model can also suffer from this issue when there are missing values in its geographic distribution of weights. Although general imputation approaches have been developed, one distinguishing fact here is that spatial imputation generally performs better for georeferenced data because it can reflect a fundamental property of those data, that is, spatial autocorrelation or spatial dependency. This paper explores how spatial imputation exploiting spatial autocorrelation can contribute to estimating missing values in a weights surface for location modeling and subsequently improve solutions for spatial optimization, specifically p-median problems using a spatially imputed weights surface. This paper examines two spatial imputation methods, ordinary co-kriging and Moran eigenvector spatial filtering. Their results are compared with conventional linear regression, essentially Expectation-Maximization algorithm results for independent observations of Gaussian random variable cases. Simulation experiments show that spatial imputation produces better results for georeferenced data than simply ignoring any missing values and non-spatial imputation, and appropriately imputed values can enhance spatial optimization solutions, regardless of the number of medians, p.

Random generation of linearly constrained fuzzy measures and domain coverage performance evaluation

The random generation of fuzzy measures under complex linear constraints holds significance in various fields, including optimization solutions, machine learning, decision making, and property investigation. However, most existing random generation methods primarily focus on addressing the monotonicity and normalization conditions inherent in the construction of fuzzy measures, rather than the linear constraints that are crucial for representing special families of fuzzy measures and additional preference information. In this paper, we present two categories of methods to address the generation of linearly constrained fuzzy measures using linear programming models. These methods enable a comprehensive exploration and coverage of the entire feasible convex domain. The first category involves randomly selecting a subset and assigning measure values within the allowable range under given linear constraints. The second category utilizes convex combinations of constrained extreme fuzzy measures and vertex fuzzy measures. Then we employ some indices of fuzzy measures, objective functions, and distances to domain boundaries to evaluate the coverage performance of these methods across the entire feasible domain. We further provide enhancement techniques to improve the coverage ratios. Finally, we discuss and demonstrate potential applications of these generation methods in practical scenarios.

Contact Force Surrogate Model and Its Application in Pantograph–Catenary Parameter Optimization

The significant increase in the speed of high-speed trains has made the optimization of pantograph–catenary parameters aimed at improving current collection quality become one of the key issues that urgently need to be addressed. In this paper, a method and solutions are proposed for optimizing multiple pantograph–catenary parameters, taking into account the speed levels and engineering feasibility, for pantograph–catenary systems that contain dozens of parameters and exhibit strong nonlinear coupling characteristics. Firstly, a surrogate model capable of accurately predicting the standard deviation of contact force based on speed and 14 pantograph–catenary parameters was constructed by using the pantograph–catenary finite element model and feedforward neural network. Secondly, sensitivity analysis and rating of the pantograph–catenary parameters under different speeds were conducted using the variance-based method and the surrogate model. Finally, by combining the sensitivity analysis results and the Selective Crow Search Algorithm, joint optimization of 10 combinations of the pantograph–catenary parameters across the entire speed range was performed, providing efficient pantograph–catenary parameter optimization solutions for various engineering conditions.

On the performance of standard nature-inspired algorithms in solving non-stationary optimization problems

Non-stationary optimization problems are a very important class of problems in many practical applications. These problems are characterized by objective functions and constraints that change with time or environmental conditions, so the optimization solution also needs to be dynamically adjusted accordingly. Many algorithms from the field of evolutionary and biology inspired computation are known as an effective approach for dealing with hard optimization problems in changing environments, that is the result of modelling of self-organized systems in nature and evolution in the biology. Natural systems always exist in the changing environments. This article aims to compare the performance of three common nature-inspired techniques, namely genetic algorithm (GA), particle swarm optimization (PSO) algorithm, and differential evolution (DE) in their standard implementation when solving non-stationary optimization problems, so as to provide a reference and rationale for subsequent selection of appropriate algorithms and improvements.

Freshness-Aware Age Optimization for Multipath TCP Over Software Defined Networks

Recently increasing real-time network applications have raised a new requirement for the future 6G network transmission, which desires the information-update packets arrive at the receiver as timely as possible. Multipath TCP (MPTCP) can be employed in the real-time update transmission system by making use of multiple paths so as to promote the information status update. However, current optimization solutions of MPTCP focus on traditional transmission performance such as throughput, delay, loss, jitter, etc. None of these works considers the freshness metric for MPTCP to satisfy the real-time demands. What's more, most works about transmission model in Software Defined Network (SDN) are based on packet-level analysis without considering flow-level situation. In order to fill these gaps, this paper proposes a novel freshness-aware age optimization solution for MPTCP (FAMT) over SDN. FAMT introduces a new four-tuples to describe the transmission state in the SDN architecture. Then FAMT classifies the whole transmission process into two phases: connection establish phase and data transmission phase. Based on the two phases, FAMT evaluates and defines the age of information for different situations. In addition, FAMT designs an innovative age-assisted fluid model of MPTCP which can realize the four goals effectively.

A design and analysis of computer experiments based mixed integer linear programming approach for optimizing a system of electric vehicle charging stations

This paper formulates a mixed integer linear programming (MILP) model to optimize a system of electric vehicle (EV) charging stations. Our methodology introduces a two-stage framework that integrates the first-stage system design problem with a second-stage control problem of the EV charging stations and develops a design and analysis of computer experiments (DACE) based system design optimization solution method. Our DACE approach generates a metamodel to predict revenue from the control problem using multivariate adaptive regression splines (MARS), fit over a binned Latin hypercube (LH) experimental design. Comparing the DACE based approach to using a commercial solver on the MILP, it yields near optimal solutions, provides interpretable profit functions, and significantly reduces computational time for practical application.

Design Improvement of Permanent Magnet Motor Using Single- and Multi-Objective Approaches

Abstract Optimisation, or optimal design, has become a fundamental aspect of engineering across various domains, including power devices, power systems, and industrial systems. Engineers and academics have been actively involved in optimising these systems to achieve better performance, efficiency, and cost-effectiveness. Optimising electrical machines, including permanent magnet motors, is a complex task. It often involves solving intricate problems with various parameters and constraints. Engineers use different optimisation methods to tackle these challenges. Depending on the specific requirements and goals of a design project, engineers may employ either single-objective or multi-objective optimisation approaches. Single-objective optimisation focuses on optimising a single objective, while multi-objective optimisation considers multiple conflicting objectives. In optimisation, objective functions are mathematical representations of what needs to be optimised. In this case, optimising the efficiency of the motor, reducing cogging torque, and minimising the total weight of active materials are defined as possible objective functions. Genetic algorithms are nature based algorithms that are commonly used in engineering to find optimal solutions to complex problems, including those with multiple objectives. In this paper, after conducting optimisations using different objective functions and methods, a comparative analysis of the results is performed. This helps in understanding the trade-offs and benefits of different design choices. Finite element analysis (FEA) is a computational method used to analyse the physical properties and behaviours of complex structures and systems. In this case, FEA is used to validate and analyse selected optimisation solutions to ensure they meet the desired characteristics and parameters. Overall, this work demonstrates the interdisciplinary nature of engineering, where mathematics, computer science (for optimisation algorithms), and physics (for FEA) converge to improve the performance and efficiency of electrical machines. It also underscores the importance of considering multiple objectives in design processes to find optimal solutions that strike a balance between competing goals.

A New Gradient-Accelerated Two-Stage Multiobjective Optimization Method for CO2-Alternating-Water Injection in an Oil Reservoir

Summary CO2-water-alternating-gas (CO2-WAG) is a reservoir development method that can simultaneously enhance oil recovery and achieve CO2 storage. However, improperly designed parameters for CO2 injection and oil production may significantly reduce the oil displacement efficiency and CO2 storage. Furthermore, optimizing the injection parameters is computationally expensive due to the high computational cost of the compositional simulation. This work aims to propose an efficient optimization method to obtain a series of well-control schemes that balance maximizing net present value (NPV) and CO2 storage for decision-makers. Given the number of CO2-WAG cycles and the duration of each cycle, we optimize the water injection rate, gas injection rate, and half-cycle for the injection well and the bottomhole pressure (BHP) for the production well. In this paper, a two-stage optimization strategy is proposed to enhance the optimization efficiency. The first stage performs the surrogate-assisted single-objective optimizations of each considered objective. It is designed to find the endpoints of the Pareto front that connect all solutions of the multiobjective optimization; this stage not only provides important search directions for the subsequent multiobjective optimization but also improves the accuracy of the surrogate model near the Pareto front. The second stage is the surrogate-assisted multiobjective optimization, which aims to find all the solutions along the Pareto front based on the Pareto endpoints obtained from the first stage. In addition, this study successfully combines the gradient of the objective functions with the meta-heuristic algorithm during the multiobjective optimization, which ensures a faster convergence to the global optimum. The proposed multiobjective optimization algorithm shows faster convergence than the conventional optimization methods when applied to the three multiobjective optimization test functions. Finally, a comparison with the conventional multiobjective optimization is conducted based on one test function and two benchmark reservoir simulation models to verify the correctness and efficiency of the proposed method. It is confirmed that the proposed method outperforms the conventional ones for the optimization of CO2-WAG injection.

Approximate solution in robust multi-objective optimization and its application in portfolio optimization

Approximate solution in robust multi-objective optimization and its application in portfolio optimization

Robust Neural Differential Solution for Time-Dependent Nonlinear Optimization With Noises Rejection

Robust Neural Differential Solution for Time-Dependent Nonlinear Optimization With Noises Rejection

Efficient Surrogate Modeling Method for Evolutionary Algorithm to Solve Bilevel Optimization Problems.

The purpose of this study was to develop an evolutionary algorithm (EA) with bilevel surrogate modeling, called BL-SAEA, for tackling bilevel optimization problems (BLOPs), in which an upper level problem is to be solved subject to the optimality of a corresponding lower level problem. The motivation of this article is that the extensive lower level optimization required by each upper level solution consumes too many function evaluations, leading to poor optimization performance of EAs. To this end, during the upper level optimization, the BL-SAEA builds an upper level surrogate model to select several promising upper level solutions for the lower level optimization. Because only a small number of upper level solutions require the lower level optimization, the number of function evaluations can be considerably reduced. During the lower level optimization, the BL-SAEA constructs multiple lower level surrogate models to initialize the population of the lower level optimization, thus further decreasing the number of function evaluations. Experimental results on two widely used benchmarks and two real-world BLOPs demonstrate the superiority of our proposed algorithm over six state-of-the-art algorithms in terms of effectiveness and efficiency.

Probabilistički višekriterijumski robustni dizajn i njegova primena u sečenju metala

Introduction/purpose: Cutting is a typical material process. However, an appropriate solution for simultaneous optimization of material machinability and tool life in material cutting processes has not been obtained yet. In this article, probabilistic multi-objective robust design (PMORD) is expounded and the robust design problem in the simultaneous optimization of material machinability and tool life is analyzed by taking the machining of ferrite-bainite dual-phase steel as an example. Methods: According to PMORD, the arithmetic mean and its deviation of various performance and utility indexes of alternative schemes are evaluated as twin independent responses, which respectively contribute a part of the partial preferable probability to the performance indexes. In the evaluation, the arithmetic average of the utility index is taken as the representative of the utility attribute, and the evaluation is made in accordance with the function or preference of the utility index. However, the deviation term is generally used as an unbeneficial type of the index (that is, the smaller the better) to participate in the evaluation. The product of the two parts of partial preferable probability is the actual partial preferable probability of the corresponding performance index. The product of the partial preferable probability of all performance utility indexes gives the total preferable probability of the corresponding scheme, which is the only index for each scheme to participate in the competition in robust design. Results: The optimization result of this example is that the tool life is 1297.3333 s (standard deviation is 2.0817 s) and the surface roughness is 2.22mm (standard deviation is 0.2mm), while the corresponding working conditions are that the heat treatment temperature of the material is 790°C, the cutting speed is 150m/min, the feed speed is 0.15mm/rev, and the cutting depth is 0.2mm. Conclusion: The example of the parameter optimization of cutting of ferrite-bainite dual-phase steel by means of PMORD indicates the rationality of the appropriate solution.

Integration of cloud, fog, and edge technologies for the optimization of high-load systems

The study is dedicated to analyzing methods and tools for optimizing the performance of high-load systems using cloud, fog, and edge technologies. The focus is on understanding the concept of high-load systems, identifying the main reasons for increased load on such systems, and studying the dependency of the load on the system's scalability, number of users, and volume of processed data. The introduction of these technologies implies the creation of a multi-level topological structure that facilitates the efficient operation of distributed corporate systems and computing networks. Modern approaches to load management are considered, the main factors affecting performance are investigated, and an optimization model is proposed that ensures a high level of system efficiency and resilience to peak loads while ensuring continuity and quality of service for end-users. The methodology is based on a comprehensive approach, including the analysis of existing problems and the proposal of innovative solutions for optimization, the application of architectural solutions based on IoT, cloud, fog, and edge computing to improve performance and reduce delays in high-load systems. The scientific novelty of this work lies in the development of a unique multi-level topological structure capable of integrating cloud, fog, and edge computing to optimize high-load systems. This structure allows for improved performance, reduced delays, and effective system scaling while addressing the challenges of managing large data volumes and servicing multiple requests simultaneously. The conclusions of the study highlight the significant potential of IoT technology in improving production processes, demonstrating how the integration of modern technological solutions can contribute to increased productivity, product quality, and risk management.

Optimization Solutions of a Multistage Balanced Transport Problem with using of the Potential Method

Route optimization is an important issue because it allows improving the economic component in production. Its generalization to optimization problems on networks is shown. The transport task is formulated in the network setting: to make a plan of cargo trans-portation on the transport network so that the cargo stocks were taken out of the points of depar-ture, the needs of consumers at the destinations were met, and the total cost of transportation was minimal. The algorithm of its solution is given, revealed on a concrete example of delivery of products to customers who sell these products on a daily basis. Tables of the basic and optimal plan, a transport network of the optimal plan of transportations are made. The process of solving such a problem by modeling in the environment is proposed. The simulation model of this process was created with using the FlexSim software. The process of creating the simulation model is demonstrated

Encoding Carbon Emission Flow in Energy Management: A Compact Constraint Learning Approach

Decarbonizing the energy supply is essential and urgent to mitigate the increasingly visible climate change. Its basis is identifying emission responsibility during power allocation by the carbon emission flow (CEF) model. However, the main challenge of CEF application is the intractable nonlinear relationship between carbon emission and power allocation. So this paper leverages the high approximation capability and the mixed-integer linear programming (MILP) representability of the deep neural networks to tackle the complex CEF model in carbon-electricity coordinated optimization. The compact constraint learning approach is proposed to learn the mapping from power injection to bus emission with sparse neural networks (SNNs). Then the trained SNNs are transformed equivalently as MILP constraints in the downstream optimization. In light of the “high emission with high price” principle, the blocked carbon price mechanism is designed to price emissions from the demand side. Based on the constraint learning and mechanism design, this paper proposes the carbon-aware energy management model in the tractable MILP form to unlock the carbon reduction potential from the demand side. The case study verifies the approximation accuracy and sparsity of SNN with fewer parameters for accelerating optimization solution and reduction effectiveness of demand-side capability for mitigating emission.