Collaborative Optimization Method Research Articles

Analysis and Improvement of Coal-loading Performance and Reliability of Thin Seam Coal Shearer Drums

The structure of coal shearer drums significantly influences the transportation efficiency and load fluctuations of thin seam coal shearers. To enhance drum efficiency and reliability, this paper proposes a collaborative optimization method based on an improved NSGA-II algorithm. Initially, using the Discrete Element Method (DEM), we analyze the impact of helical blades on transportation performance and investigate novel designs for two types of coal-guiding drums. To optimize dynamic performance, concepts like spatial density and adaptive radius are introduced, proposing an NSGA-II enhancement strategy based on spatial density for the collaborative optimization of drum structure and motion parameters. Finally, the entropy-weighted TOPSIS method is used for subjective evaluation to select the optimal solution. Research findings demonstrate that this collaborative optimization method significantly enhances drum reliability, reducing load fluctuations by 46.34%, increasing transportation efficiency by 7.06%, and decreasing transportation power by 27.31%.

Composition and Injection Rate Co-Optimization Method of Supercritical Multicomponent Thermal Fluid Used for Offshore Heavy Oil Thermal Recovery

Supercritical multicomponent thermal fluid injection is a new technology with great potential for offshore heavy oil thermal recovery. In the process of thermal fluid generation, the reaction conditions including temperature, pressure, and the organic mass concentration in the reaction material will significantly affect its composition and injection rate and will further affect the thermal recovery and development quality of heavy oil. However, there is a lack of relevant research on the variation rules and control methods of the composition and injection rate of supercritical multicomponent thermal fluids, resulting in a lack of technical mechanisms for effective optimization. To fill this gap, a reaction molecular dynamics simulation method was used to simulate thermal fluid generation under different temperatures, pressures, and organic mass concentrations. The changes in thermal fluid composition and yield with reaction conditions were studied, and a control model of thermal fluid composition and yield was established. The proportional relationship between the thermal fluid generation scale of an offshore heavy oil platform and the simulated thermal fluid generation scale is analyzed, and a collaborative optimization method of thermal fluid composition and injection rate in field applications is proposed. The results show the following: (1) The higher the mass concentration of organic matter, the higher the content of supercritical carbon dioxide and supercritical nitrogen in thermal fluids, and the lower the content of supercritical water. (2) The higher the temperature and pressure, the higher the thermal fluid yield, and the higher the organic mass concentration, the lower the thermal fluid yield. (3) The component fitting model conforms to the power function relationship, and the coefficient of determination R2 is greater than 0.9; the yield fitting model conforms to the modified inverse linear logarithmic function relationship, the determination coefficient R2 is greater than 0.8, and the fitting degree is high. (4) The ratio between the actual injection rate of thermal fluids in the mine field and the molecular simulated thermal fluid yield is the ratio of organic matter mass in the platform thermal fluid generator and organic matter mass in the simulated box. (5) Based on the composition and yield control model, combined with the simulation of the ratio relationship between yield and injection rate in the field, a collaborative optimization method of thermal fluid composition and injection rate was established. The research results can provide an effective technical method for predicting, controlling, and optimizing the composition and injection rate of supercritical multicomponent thermal fluids.

Dynamics analysis and collaborative optimization of vehicle steering mechanism

In order to improve the dynamic response characteristics of the steering mechanism, a research scheme for increasing the natural frequency based on lightweight design was proposed. Based on the finite element method and the collaborative optimization method, the modal characteristics and harmonic response characteristics of the model were studied and analyzed to verify the strength and stiffness performance of the optimized structure. The modal shapes between the free mode and the constrained mode were compared and analyzed. With the second-order natural frequency as the optimization objective, the response surface function of the equivalent stiffness was constructed. Through optimization calculation, the design variables that satisfy the constraint conditions can be obtained. The results show that the optimized structure can increase the second-order natural frequency by 14.4 % on the premise of reducing the mass by 5.2 %, effectively avoiding the excitation frequency of the engine.

Collaborative optimization for energy hub and load aggregator considering the carbon intensity-driven and uncertainty-aware

To achieve optimization operation between the energy hub operator and the load aggregators, this paper proposes a collaborative model for the energy hub and load aggregator considering the carbon intensity-driven and uncertainty. Firstly, a time-coupled carbon emission flow model for energy storage is proposed to cope with the change in carbon intensity at the injection node. Meanwhile, a carbon intensity-based incentive price is developed for interruptible loads with diverse carbon intensity. Secondly, the Wasserstein metric-based distributionally robust optimization method is leveraged to height against the energy hub uncertainty, in which the infinite-dimensional distributionally robust optimization model is reformulated into a tractable linear programming. Thirdly, considering the multi-entity characteristics of energy hub and load aggregators, a collaborative optimization method based on the alternating direction method of multipliers algorithm is constructed. Finally, to meet the convergence requirements of the alternating direction method of multipliers algorithm, the iterative convexification algorithm is applied to transform the nonconvex bi-level energy hub-users model into an iterative convex model by avoiding binary variables and big-M parameters. Consequently, the energy hub and load aggregator can operate cooperatively and protect privacy in uncertain conditions. Simulation results verify the effectiveness of the proposed model and method. Simulation results demonstrate the effectiveness of the proposed model and method. Specifically, the iterative convexification algorithm achieved a profit of ¥1653.82 for the load aggregator, which is 24.4 % higher than the profit obtained by CONOPT (¥1329.73) and 20.3 % higher than that obtained by IPOPT (¥1374.69), thus providing substantial advantages over traditional nonlinear solvers.

Collaborative Optimization of a Matrix Manufacturing System Based on Overall Equipment Effectiveness

When several traditional flow-shop lines operate in parallel, the operation mode with no communication between production lines will no longer be the optimal production paradigm. This paper describes matrix manufacturing systems (MMS) in a general manner from the perspective of related works, comparing different manufacturing organizational forms and their characteristics. Subsequently, MMS are extracted during the parallel production of multiple surface mount technology (SMT) lines. An overall equipment effectiveness (OEE) online calculation model and a collaborative optimization method are proposed based on the OEE of the MMS. The innovative idea of this study is to divide existing multiple parallel SMT lines into MMS. The efficiency of each matrix unit (MU) was calculated, and a collaborative optimization method was proposed based on an indicator (OEE). In this paper, an example of eight SMT lines is presented. The partitioning of MUs, OEE calculation of each MU, and the low OEE unit collaborative optimization method are described in detail. Through a case study, the architecture of the collaborative optimization model for the MMS was constructed and discussed. Finally, the improvement in the OEE proved the effectiveness and usability of the proposed architecture.

Tumor classification algorithm via parallel collaborative optimization of single- and multi-objective consistency on PET/CT

Malignant tumors still have a high incidence and mortality rate worldwide. Pathological examination remains the clinical gold standard for tumor diagnosis. However, some patients cannot undergo pathological examination due to advanced age and special lesion location. Therefore, making full use of PET/CT to assist doctors in tumor classification has important clinical significance. Since category labels are calibrated according to pathological images, it is difficult to obtain effective pathological category features directly using PET-CT image modeling. In response to this problem, this paper proposes a novel tumor classification algorithm. This method fully utilizes multi-gray-level 3D gray-level co-occurrence matrix and the proposed rough and fine constraint network under the constraint loss of rough and fine labels. Based on single- and multi-objective consistency, a parallel collaborative optimization method is proposed, including category consistency loss and feature specificity loss. To reduce the interference of redundant features, an improved Boruta feature selection method using multiple classifiers and multiple steps is proposed. The final result is obtained through a conditional weighted voting function. The proposed method shows excellent performance in both the submodels and the fusion model. We validated the proposed tumor classification method on three datasets and achieved good performance with the accuracy of 0.80–0.85 and F1-score of 0.78–0.88. The results indicate that the proposed method has good performance and generalization ability.

Enhanced thermally integrated Carnot battery using low-GWP working fluid pair: Multi-aspect analysis and multi-scale optimization

Thermally integrated Carnot battery (TICB) emerges as a promising technology for long-duration energy storage. This study aims to improve its multi-dimensional performance from the optimization design level, thereby promoting its competitiveness. Firstly, a refined system architecture is developed, integrating subsystem layout modification and advanced thermal integration mode. Sixteen potential working fluid pairs are formed by screening low-GWP refrigerants as drop-in substitutes for R245fa. Then, a comprehensive evaluation framework is established to characterize this technology from energy, exergy, and economic perspectives, and a collaborative optimization method is introduced to facilitate integrated optimization. Finally, a whole cycle cumulative exergy analysis is conducted to explore directions for further improvement. Results indicate that, compared to the basic architecture, the round-trip efficiency (RTE) of the proposed system is elevated by over 30%. The use of different alternative refrigerants in the charging and discharging cycles makes the TICB not only sustainable but also more efficient. The thermodynamic dilemma of determining the heat pump evaporation temperature disappears with a closed-type heat source. To maximize overall thermo-economics, sufficient heat pump subcooling and evaporator internal superheat are necessary, and the ideal storage temperature span and pinch point temperature difference of heat exchangers must be identified. The collaborative optimization displays that the first-rank working fluid pair is R1234ze(Z)-R1224yd(Z), with an RTE of 85.2%, a volumetric energy density of 3.10 kWh/m3, and a levelized cost of storage of 0.303 $/kWh, respectively. Furthermore, the RTE is highly sensitive to the insulation quality of storage tanks. A mere 2.5% performance degradation can lead to a 10% points decline in RTE. Internal exergy destruction is primarily caused by poor heat transfer matching, while external exergy loss mainly results from underutilized heat sources during non-charging periods. Therefore, we recommend the district heating network as the target heat source for the dual-period TICB. These achievements offer a valuable reference for the research and development of efficient, affordable, and sustainable TICB.

A Collaborative Neurodynamic Optimization Approach to Distributed Chiller Loading.

In this article, we present a collaborative neurodynamic optimization approach to distributed chiller loading in the presence of nonconvex power consumption functions and binary variables associated with cardinality constraints. We formulate a cardinality-constrained distributed optimization problem with nonconvex objective functions and discrete feasible regions, based on an augmented Lagrangian function. To overcome the difficulty caused by the nonconvexity in the formulated distributed optimization problem, we develop a collaborative neurodynamic optimization method based on multiple coupled recurrent neural networks reinitialized repeatedly using a meta-heuristic rule. We elaborate on experimental results based on two multi-chiller systems with the parameters from the chiller manufacturers to demonstrate the efficacy of the proposed approach in comparison to several baselines.

Collaborative optimization of renewable energy power systems integrating electrolytic aluminum load regulation and thermal power deep peak shaving

Addressing renewable energy (RE) curtailment in power systems necessitates a comprehensive strategy leveraging peak regulation resources from both the power and load sides. On the power side, deep peak shaving of thermal power plants can mitigate surplus electricity during periods of high RE production. On the load side, energy-intensive industrial loads, characterized by large capacity and rapid adjustability, can respond effectively to energy deficits when RE is low. To enhance the peak regulation capacity for optimal RE accommodation, this paper proposes a collaborative optimization method combining electrolytic aluminum load (EAL) regulation with thermal power deep peak shaving (DPS). The study is initiated by developing a sophisticated peak regulation model for the electrolytic aluminum load (EAL), considering production characteristics, cost features, and safety constraints. Subsequently, a collaborative optimization model is formulated, integrating EAL regulation with thermal power deep peak shaving (DPS), aiming to minimize societal peak regulation costs (PRC). Applying this model to a practical regional grid in Yunnan Province reveals substantial reductions in RE curtailment rates, effective minimization of total social PRC, and enhanced operational economics of thermal power DPR under varying wind power scenarios. Notably, the proposed approach requires only minor adjustments to the EAL, ensuring production safety is maintained. This research provides valuable insights for the seamless integration of EAL into grid peak regulation services.

Human–machine collaborative optimization method for dynamic worker allocation in aircraft final assembly lines

The allocation of workers with diverse skills on assembly stations significantly impacts the efficiency of aircraft final assembly (AFA). In a real-life assembly process, workers are allowed to move between multiple stations, to avoid bottleneck stations. The arrangement of worker movement is a practical issue coupled with the worker allocation, but is still ignored. This study formulates a dynamic worker allocation problem in AFA (dWA-AFA), focusing on the multiobjective joint optimization of worker allocation and worker movement. The trade-off between objectives depends on the human preferences that reflect assembly requirements. To address this, a human–machine collaborative optimization method (HMC-O) is proposed, involving a bidirectional collaboration: machine-to-human preference adaption, and human-to-machine experience support. Specifically, we design an adaptive dominance operator for integrating human preferences, and combine it with a two-stage nondominated sorting approach to generate initial optimization solutions. An experience-driven neighborhood search is further developed, which uses human experience to improve solutions. The proposed method is evaluated through two scales of real cases. It is observed the dWA-AFA significantly reduces the takt time, particularly by 14.21 % and 9.25 % in two worker-shortage scenarios respectively. Meanwhile, the HMC-O is competitive in terms of convergence and preference satisfaction for solving dWA-AFA.

Pattern reconfigurable antenna with specified main lobe deflection and stable bandwidth by using bistable composite laminates

Pattern reconfiguration of antennas has become a very important measure to improve the signal gain and working bandwidth by manipulating beam direction. Developing rational methods to find the reconfigurable structure is a key problem. In this paper, a collaborative optimization method is proposed to comprehensively consider both the geometric parameters of the bistable substrate and the size of the radiation patch. This method enables the design of a pattern reconfigurable antenna with specified main lobe deflection and stable bandwidth. Specifically, by using a two-step process, the log-periodic dipole antenna (LPDA) is conformally mapped from the planar substrate to the bistable substrate. Further investigation reveals that the main lobe deflection angle and bandwidth stability are influenced by the geometric parameters of the bistable substrate and the size of radiation dipoles, respectively. Thus, these parameters are selected as design variables for solving the proposed collaborative optimization model. The transformation between two stable configurations enables the proposed LPDA to deflect the main lobe of the H-plane pattern by 30° while maintaining consistency in the E-plane patterns. Importantly, the resonant frequencies remain unaffected and the bandwidth does not decrease during the pattern reconfiguration. Notably, the pattern reconfigurable mechanism is rooted in that the transformation between the two stable configurations alters the number and position of the dipoles in the radiation region and their current path, thereby changing the radiation direction of electromagnetic waves. The proposed collaborative optimization method has a potential application for other types of antennas and offers opportunities for various applications in the field of wireless communication.

Collaborative design method for multi-impeller turbomachinery based on variable sectional-area distribution

By establishing the characterization model of variable sectional area distributions, a cascade design method of torque converter based on variable sectional area distribution is proposed. Additionally, the corresponding collaborative design method for multi-impeller turbomachinery is proposed and further studied. The design cases of the impellers with different sectional area distributions are constructed through the response surface method, and the corresponding hydrodynamic characteristics are further compared and explored. However, the design of different impeller sectional area distributions for the optimal performance of the whole turbomachinery requires the collaborative design of each component’s distribution results rather than a simple combination of such optimal impeller’s area distribution result. Thus a collaborative design strategy is proposed, which can improve the pump’s capacity performance by 8.83%, the maximum torque ratio performance can also increased by 6.33%, which proves that such a collaborative design method is an effective optimization design approach to improve the performance of the torque converter without altering the overall dimension. The experimental results of the optimal prototype show that the torque ratio reaches 5.33% higher than the original value, which indicates that such a collaborative optimization method for multi-impeller turbomachinery is effective and reliable.

Energy–Logistics Cooperative Optimization for a Port-Integrated Energy System

In order to achieve carbon peak and neutrality goals, many low-carbon operations are implemented in ports. Integrated energy systems that consist of port electricity and cooling loads, wind and PV energy devices, energy storage, and clean fuels are considered as a future technology. In addition, ports are important hubs for the global economy and trade; logistics optimization is also part of their objective, and most port facilities have complex logistics. This article proposes an energy–logistics collaborative optimization method to fully tap the potential of port-integrated energy systems. A logistics–energy system model is established by deeply examining the operational characteristics of logistics systems and their corresponding energy consumption patterns, considering ships’ operational statuses, quay crane distribution constraints, and power balances. To better represent the ship–energy–logistics optimization problem, a hybrid system modeling technique is employed. The case of Shanghai Port is studied; the results show that costs can be reduced by 3.27% compared to the traditional optimization method, and a sensitivity analysis demonstrates the robustness of the proposed method.

Co-optimization method for injection strategy of underground natural gas storage integrating aboveground and underground parts

Underground natural gas storage (UNGS) is an important infrastructure to overcome the imbalance between supply and demand of natural gas. During the gas injection period, gas is compressed aboveground and then transported to injection wells for injection into the underground reservoir. Reasonably formulating the compressor operation scheme and injection scheduling scheme is a crucial step. However, “Compressor-pipeline-injection well-reservoir" constitutes a synergistic whole. More efficient and practical operation schemes can only be proposed through the establishment of an optimization method that coordinates the entire gas injection process. A novel two-layer multi-objective collaborative optimization method is proposed to optimize the monthly operation scheme during the injection period. The upper-layer model focuses on optimizing injection volume allocation to minimize reservoir pressure deviation and total compressor energy consumption. The under-layer model optimizes the compressor operation scheme by considering the multi-stage compression (MC) and clearance adjustment (CA) equipped in reciprocating compressors. The synergistic relationship during the injection process is integrated into the traditional model through the interlayer iteration method. Furthermore, a high accuracy and efficiency proxy model based on machine learning algorithms (ML) is developed to predict compressor performance. The results of the case study indicate that, compared with traditional optimization method and the two-stage co-optimization method, the proposed method achieves an average energy consumption reduction of 9.54 × 106 kWh (10.79%) and 2.12 × 106 kWh (2.4%) respectively. The effective utilization of CA and MC can significantly alleviate the deviations between displacement and processing requirements, eliminating 22,120.5 × 104 Nm3(85.62%). The two-layer co-optimization method effectively integrates the aboveground and underground parts of UNGS, providing a solution for safe and efficient operation during the injection period.

A two-stage collaborative optimization method for emergency repair station planning and recovery strategy of cyber-physical-transportation networks

In this paper, a two-stage collaborative stochastic optimization method for pre-disaster emergency repair station planning and post-disaster recovery strategy of cyber-physical-transportation networks is proposed in this paper to enhance the resilience under extreme scenarios. In the first stage, based on the principle of hierarchical portioning, the responsible area for the emergency repair station is divided by the community theory, and the emergency repair station planning model is developed. In the second stage, the time sequence of repairing the faulty components is first established based on the graph theory. Then, based on the spatial distribution of adjacent time-series faulty components on the transportation network, a faulty component restoration time model is established to accurately reflect the faulty restoration time. On this basis, the functional coupling effects of economic dispatch control and substation automation system failures on load recovery are analyzed to establish a load recovery model. The case studies of the IEEE RTS-79 test system demonstrate that, compared with traditional independent recovery model, the model proposed in this paper performs better in enhancing resilience under extreme scenarios.

Optimal design of hydrogen production processing coupling alkaline and proton exchange membrane electrolyzers

This study considered complementation of the different electrolyzers and designed a new hydrogen production system coupling alkaline (ALK) electrolyzers and the proton exchange membrane (PEM) electrolyzers. A collaborative optimization method is proposed to design both the capacities and the annual energy scheduling of the system. Due to the different operation characteristics and parameters of the two electrolyzers, the operation states of them are described separately in the model. Besides, several practical factors are also considered, including electrolyzer grouping, hydrogen production loss, adjustment response efficiency and switch-on times. According to the calculation results, the annual income of the coupling system is 6.0 % and 28.9 % higher than that of the ALK only and PEM only system. The installed load rates of PEM electrolyzers to the coupling systems are between 10 and 20 %. Furthermore, the influences of multiple factors on the coupling system are also analyzed. After analysis and comparison, hydrogen production and system economy rise with increased wind power installed proportion is rising. Besides, the increase of the allowed proportions of electricity purchase can enhance the stability of system power supply, and improve the hydrogen production and economy.

Collaborative robust topology optimization of FGMs considering hybrid bounded uncertainties based on the distance to ideal solution

In this article, an efficient collaborative robust topology optimization (CRTO) method is proposed for functionally graded materials (FGMs) considering hybrid bounded uncertainties (HBU). Firstly, to realize the collaborative optimization of the structural topology and volume fraction of reinforcement in FGMs, two sets of design variables are defined following the SIMP framework. Secondly, with the uncertain material properties and external loads mathematically modeled as random and interval uncertain parameters respectively, the objective performance of FGMs are described as the implicit function of two sets of design variables and uncertain parameters. In contrast to the classical method of weighting the mean and standard deviation of objective performance, the robust objective function is constructed based on a novel distance to ideal solution to avoid the manually setting of weight parameters. The sensitivity analysis of the objective and constraint function with regard to the two sets of design variables is derived explicitly and a realization vector set is defined for bounded probabilistic uncertainties to parallelize the sensitivity analysis. In addition, an adaptive density shift algorithm is proposed to produce a clear topological profile and accelerate the convergence of the optimization. The effectiveness of the proposed method is demonstrated by both numerical and engineering examples.

Collaborative optimization of reference powers and droop coefficients in DC distribution networks considering flexibility enhancement

In droop-controlled DC distribution networks (DCDNs), the uncertainties of loads and renewable energy powers worsen the system performances. This paper proposes a collaborative optimization method of reference powers and droop coefficients to optimize more objectives that including reducing line losses, improving the voltage level, and enhancing the flexibility of DCDNs. Firstly, a fast and accurate flexibility margin evaluation method is presented for DCDNs, in which the allowable power variation intervals under operational constraints are used to quantify the flexibility margins. The analytic relations of allowable power variation intervals and operational constraints are established by the linear power flow model, and the flexibility margins can be calculated quickly and directly. Secondly, the bi-level collaborative optimization method of reference powers and droop coefficients is proposed to optimize more objectives simultaneously. The reference powers are optimized to improve the voltage level and reduce line power losses in the upper-level optimization, and the droop coefficients are optimized to enhance the system flexibility in the lower-level optimization. Finally, multiple test systems with different topologies are simulated in detail. The case study demonstrates that: 1) The flexibility evaluation method can quantify the margins of operational constraints quickly and accurately. Compared with the bisection method, the calculation time of proposed flexibility evaluation method reduce 99.52% with the error of 1.68%. 2) The proposed collaborative optimization of reference powers and droop coefficients can effectively reduce line losses and the risks of voltage off-limit and VSC overload of DCDN under uncertainties.

Data-driven hierarchical collaborative optimization method with multi-fidelity modeling for aerodynamic optimization

The aerodynamic optimization of aircraft based on evolutionary algorithms generally requires thousands or even tens of thousands of iterations. Moreover, it faces challenges such as high-dimensional design variables and high computational costs, leading to low efficiency in algorithm search and poor optimization results. In order to enhance the efficiency of aerodynamic optimization, a data-driven collaborative optimization and multi-fidelity hierarchical modeling method is proposed. Firstly, a Clustering-Enhanced Teaching-Learning-Based Optimization (CETLBO) algorithm is introduced to improve its exploration and exploitation capabilities. By employing a collaborative optimization strategy, a divide-and-conquer paradigm is utilized to efficiently solve high-dimensional, nonlinear, and complex engineering optimization problems. Secondly, a two-stage hierarchical aerodynamic optimization framework is designed to reduce computational costs by integrating data from different fidelity levels. Stage 1 adopts a data-driven approximate optimization method to provide prior knowledge for the high-fidelity optimization of stage 2. Finally, the proposed method is applied to the optimization design of supersonic wing shapes. Compared to traditional optimization design systems, it demonstrates high efficiency in high-dimensional aerodynamic optimization problems, yielding better optimization results.

A Collaborative Neurodynamic Optimization Approach to Distributed Nash-Equilibrium Seeking in Multicluster Games With Nonconvex Functions.

In this article, we propose a collaborative neurodynamic optimization (CNO) method for the distributed seeking of generalized Nash equilibriums (GNEs) in multicluster games with nonconvex functions. Based on an augmented Lagrangian function, we develop a projection neural network for the local search of GNEs, and its convergence to a local GNE is proven. We formulate a global optimization problem to which a global optimal solution is a high-quality local GNE, and we adopt a CNO approach consisting of multiple recurrent neural networks for scattering searches and a metaheuristic rule for reinitializing states. We elaborate on an example of a price-bidding problem in an electricity market to demonstrate the viability of the proposed approach.