To enhance the accuracy of short-term photovoltaic (PV) power forecasting, this study proposes a novel hybrid model that integrates Random Forest (RF), Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Sample Entropy (SE), the Random Walk with Compulsory Evolution (RWCE) algorithm, and the Gated Recurrent Unit (GRU) network. Initially, RF is applied to select relevant meteorological features, minimizing redundancy and improving both training efficiency and predictive robustness under complex operating conditions. ICEEMDAN is then employed to decompose the PV power series into multiple quasi-stationary components, mitigating the adverse effects of non-stationarity on forecasting accuracy. Following this, SE is applied to quantify the complexity of each component and reconstruct the decomposed signals into high-, mid-, and low-frequency bands, simplifying the inputs to the forecasting model. To further improve performance, the RWCE algorithm optimizes GRU network hyperparameters through global exploration, individual evolution, and enforced evolution strategies. The optimized GRU network then predicts each reconstructed component, and the component-wise forecasts are aggregated to yield the final PV power output. Simulation results from several representative months indicate that the proposed approach reduces RMSE by an average of 9.02% compared to comparison model and by 43.41% relative to the baseline model, demonstrating its superior forecasting capability. Additionally, the model demonstrated scalability across varying climate conditions, confirming its applicability in real-world scenarios.

The simultaneous optimization of multiple integrated systems is a key challenge in modern industrial processes. The random walk algorithm with compulsive evolution has shown significant progress in addressing combined heat and mass exchanger network synthesis (CHMENS) problems by offering each optimization variable with the stochastic walk size and direction. The evolution of coupling parameters is related to the development of the two sub-networks and affects the global optimization performance of CHMENS. Thus, this paper proposes an enhanced specific-evolution strategy based on the coupling variables, which is designed to improve the global and local search capabilities through the alternate evolution of mass exchange temperature and mass flowrate using small step lengths. Additionally, a parameter tuning scheme promoted by adjusting the control parameters is theoretically analyzed and evaluated through multi-group coordinated experiments. Finally, the enhanced specific-evolution strategy integrated with the parameter tuning scheme is applied to two case studies to validate its subsystem optimization performance. In both cases, multiple promising structures were obtained, and the optimal solutions achieved lower total annual cost than most previously reported in the literature. These results demonstrate the effectiveness of the proposed method in facilitating structural evolution and global optimization in CHMENS.

This paper proposes an innovative simultaneous optimization approach for single and multi-component mass exchanger network synthesis (MENS). A retrofitted stage-wise superstructure and a parallelized random walk algorithm with compulsive evolution (RWCE) are adopted. An iterative calculation method is designed to satisfy the requirements of multi-component mass transfer, with a relaxation for the outlet composition of the lean streams. The parametric analysis shows that the relaxation coefficient plays a major role in driving the convergence of the method. To improve the robustness of the established model, an adaptive relaxation coefficient strategy is implemented for multi-component MENS problems. In a divergence situation, the outlet concentration of the lean stream can be adjusted automatically by a random relaxation coefficient. Finally, three industrial MENS examples are considered in this work, whose total annual cost (TAC) are reduced by 7179, 2212, and 551 $·year –1. The corresponding optimization times are obtained to be 336, 125, and 145 s. The results indicate improvements in the economy and time, demonstrating that the parallelized RWCE can yield an optimal TAC and optimization efficiency compared to previous results. Overall, the adaptive relaxation coefficient strategy enhances the convergence for multi-component MENS problems.

Chemical production processes involve complex workflows, making it challenging to balance equipment investment costs and external energy consumption effectively. A robust heat exchanger network (HEN) integration approach can comprehensively address the design of equipment layout, utility usage, and the efficient utilization and recovery of energy, facilitating optimal designs that reduce energy consumption in chemical processes while enhancing the energy efficiency of process fluids. This study proposes a novel optimization framework, utilizing a step-size adaptive evolutionary heuristic algorithm that minimizes the reliance on parameter tuning during optimization. Separate fixed investment cost relaxation penalty methods are developed for new equipment, existing equipment, and utility heat exchangers, alleviating structural evolution challenges caused by abrupt cost fluctuations during the addition or removal of equipment. To tackle the complexity of the optimization process and the uncertainty of the solution space, metrics for evaluating structural evolution capability are designed to dynamically guide the diversity of optimization paths and improve the global optimization performance of the algorithm. An analysis of five benchmark cases, including a gasoline-fractionating plant and the Bandar Imam aromatic plant, demonstrates that the proposed method consistently outperformed (saving between 1.02 % and 5.4 %) or is closely comparable (with deviations of 0.003 % (H13C7) and 0.005 % (H8C7)) to the best results reported in the open literature. These findings validate the feasibility of the new approach across various complex models, providing a novel and effective pathway for optimizing HEN in chemical processes.

Heat exchanger network synthesis in process system engineering poses a significant challenge, primarily because of the intricacies of stream matches and the nonlinear nature of continuous variables. Numerous heuristic methods are frequently trapped in local optima because of their greedy acceptance criteria, which limits their capacity to explore diverse solutions. This aspect of enhancing algorithms through acceptance Criterion has yet to be considered in previous studies. Hence, a damping strategy using damping perfect solutions is proposed and applied to the Random Walk Algorithm with Compulsive Evolution, which has recently received considerable attention. This strategy aims to prevent trapping in local optima and improve global search capabilities, thereby reducing the risk of premature convergence to the local optima. To address the impact of different damping coefficients within the strategy on the optimization process and structural variations in the algorithm, three damping strategies were designed: a fixed damping strategy with a constant damping coefficient, a variable damping strategy featuring variable damping coefficients at different optimization stages, and an adaptive damping strategy that tracks structural changes and selectively delays modifications driven by continuous variables to preserve those driven by integer variables. The results obtained from applying these strategies to three classical industrial HEN cases are 2,890,884 $/yr, 6,650,082 $/yr, and 1,709,205 $/yr, respectively. These results outperformed the optimal outcomes documented in the literature, demonstrating the effectiveness of the new method.

Designing efficient heat-exchanger networks is essential for the effective use of energy in the process industries, fostering sustainable and low-carbon development. Traditional heuristic algorithms often fail to balance global and local search processes adequately when solving heat exchanger network synthesis. This frequently leads to early structural fixation and limits the exploration of integer variable search spaces. This study presents an innovative structural enhancement approach, incorporating a dynamic penalty for utility operating costs and an adaptive evolution strategy. The developed method increases the number of heat exchangers by dynamically penalizing utility operating costs and allows evolutionary parameters to be adaptively adjusted based on historical solutions. This approach diminishes the dependence on manual parameter tuning while enhancing flow matching diversity. Moreover, novel metrics for assessing structural diversity, based on node-based nonstructural model with stream splitting are established to facilitate the transition between various optimization paths, thereby improving the algorithm's robustness throughout different stages of the optimization process. Finally, computational analysis on five industrial-scale cases yields more optimal solutions with varied structural configurations compared to previous studies, demonstrating the method's feasibility and effectiveness.

The generation of new heat exchangers during optimization is the main driving force for the structural evolution of the heat exchanger networks. However, the new structure formed by randomly generating heat exchanger using a basic algorithm has only a limited reduction in the target cost, and this aspect has rarely been considered in previous studies. Therefore, this study proposes a new heuristic algorithm with a dynamic generation strategy, to improve the structure evolution capability of heat exchanger networks. The split ratio accompanying the generation strategy based on the concept of the dynamic equilibrium point is established, which improves the generation performance of new heat exchanger. To address the problem of heat exchangers with coupling relationship that are easy to be eliminated in the later stage of optimization due to relatively small heat load, an incentive heat load strategy is designed to coordinate with the former method to provide a certain opportunity for the new heat exchanger to be allocated a large heat load. Compared to the optimal results listed in the literatures, the results of three industrial network cases obtained by the proposed method are 1,711,806 $/yr, 6,665,342 $/yr, and 1,385,391 $/yr. This demonstrates that the proposed scheme improves the global search ability of the basic algorithm, promoting individual structure disturbance and competitiveness of new heat exchange units.

The impeller, as a key component of artificial heart pumps, experiences high shear stress due to its rapid rotation, which may lead to hemolysis. To enhance the hemolytic performance of artificial heart pumps and identify the optimal combination of blade parameters, an optimization design for existing pump blades is conducted. The number of blades, outlet angle, and blade thickness were selected as design variables, with the maximum shear stress within the pump serving as the optimization objective. A back propagation (BP) neural network prediction model was established using existing simulation data, and a grey wolf optimization algorithm was employed to optimize the blade parameters. The results indicated that the optimized blade parameters consisted of 7 impeller blades, an outlet angle of 25 °, and a blade thickness of 1.2 mm; this configuration achieved a maximum shear stress value of 377 Pa-representing a reduction of 16% compared to the original model. Simulation analysis revealed that in comparison to the original model, regions with high shear stress at locations such as the outer edge, root, and base significantly decreased following optimization efforts, thus leading to marked improvements in hemolytic performance. The coupling algorithm employed in this study has significantly reduced the workload associated with modeling and simulation, while also enhancing the performance of optimization objectives. Compared to traditional optimization algorithms, it demonstrates distinct advantages, thereby providing a novel approach for investigating parameter optimization issues related to centrifugal artificial heart pumps.

Heat exchanger network (HEN) synthesis is a vibrant research field in process system engineering, with substantial contributions to energy conservation and emissions reduction initiatives. The optimal design of a heat exchanger network is not an easy task due to the abundance of local optima in the solution space caused by the non-linear, non-convex, and discontinuous nature of the problem. Generally, several heuristic algorithms employ a greedy evolutionary mechanism, optimize through greedily accepting the decrease in the objective function, and converge to obtain the optimal solution. The Random Walk algorithm has a simple evolutionary mechanism, is prone to mutation, and exhibits high flexibility. However, the algorithm's inherent persistent greediness in searching restrict the scope of the search. Thus, this paper proposes an anti-greedy concept based on the Random Walk method to serve as the basis of a new synthesis approach called the Anti-greedy Random Walk algorithm. Two strategies are proposed in the algorithm, which broaden the solution domain by slowing down rapid unit reduction and accepting imperfect solutions, respectively. One strategy is to thoroughly search for the integer and continuous variables of the HEN problem by covering a much larger search space. Another is to escape the local extrema and move forward to discover more possibilities. Quantitative data demonstrates the algorithm's ability to avoid the local extrema and enhance the search effectiveness. Three different scales of classical cases are used in this work and the obtained results are superior to the published ones.

The non-structural model of heat exchanger networks (HENs) offers a wide solution space for optimization due to the random matching of hot and cold streams. However, this stochastic matching can sometimes result in infeasible structures, leading to inefficient optimization. To address this issue, a tabu matching based on a heuristic algorithm for HENs is proposed. The proposed tabu-matching method involves three main steps: First, the critical temperature levels-high, medium, and low-temperature intervals-are determined based on the inlet and outlet temperatures of streams. Second, the number of nodes is set according to the temperature intervals. Third, the nodes of streams are flexibly matched within the tabu rules: the low-temperature interval of hot streams with the high-temperature interval of cold streams; the streams crossing cannot be matched. The results revealed that by incorporating the tabu rules and adjusting the number of nodes, the ratio of the feasible zone in the whole solution domain increases, and the calculation efficiency is enhanced. To evaluate the effectiveness of the method, three benchmark problems were studied. The obtained total annual costs (TACs) of these case studies exhibited a decrease of USD 4290/yr (case 1), USD 1435/yr (case 2), and USD 11,232/yr (case 3) compared to the best published results. The results demonstrate that the proposed tabu-matching heuristic algorithm is effective and robust.

质量交换网络(MEN)作为过程集成系统的新兴分支方向,具有提高资源利用效率和降低环境污染的双重效益.目前典型的超结构模型在MEN优化中已经取得了很好的应用,但超结构固定排列方式限制了模型中换热器匹配的灵活性和求解域的扩大.基于此,提出了一种新的有分流节点非结构模型,以节点的形式量化传质单元的匹配方式,实现传质单元在流股上的自由匹配,从而增强了单元匹配的灵活性.同时考虑到实际工程中有分流情况的出现,以分组数和分支数表示传质单元的有分流形式,进一步扩大了模型的求解范围.最后,通过2个实际MEN算例分析计算,验证了有分流节点非结构模型的有效性.

根据新工科对新时代人才的培养要求,需注重和强调人才技能培养和创新创业人才培养.利用信息化技术,借助混合式教学体系,充分利用好学生的课前、课中、课后时间,将课堂知识与课外学习有机结合起来,合理分配学生的学习时间,注重应用与创新模式的革新,对学习内容、课堂组织、课堂考核等模块进行精心设计,深入挖掘混合式教学设计的关键环节,探究智慧环境下工科专业的课堂教学模式,建立以学习为中心的多元化、激励型、可持续性的评价体系,提高教学质量和学习效果,培养未来创新型卓越工程人才.

建立一种拓展节点非结构模型,在冷热两端,各额外增设一条冷热流股和常规热冷流股进行匹配,判定冷热内部公用工程的生成,实现公用工程在冷热流股上任意位置的独立匹配,提升模型自由度.将强制进化随机游走算法引入到实际工程算例 9SP、10SP、15SP 中进行优化,最优结果比现有文献分别节省了 5600$·a-1、848$·a-1、4455$·a-1.该拓展模型的应用不仅可以得到更经济的换热网络设计,并且具有更强的灵活性.

解冻技术对肉类品质有一定程度的影响,不同解冻技术会导致肉类食品的色泽、含水量、pH值、口感、汁液流失以及风味等因素产生不同程度的变化.因此,根据不同肉类的特性选取合适的解冻技术和工艺,保证解冻后的肉类能尽可能多的保留其新鲜度、降低因汁液流失、变味以及营养物质的丢失而影响口感,保证工业生产的质量及标准,从而满足高质量生活的需求.本文综述了新型解冻技术如微波解冻、超声波解冻、高压静电场解冻等分别对猪肉、鸡肉、牛肉品质的影响,探讨了解冻食品质量的评价指标以及不同解冻技术对肉类品质的影响,为解冻技术在肉类食品工业化中推广应用提供技术理论支撑.

PLC在制冷空调控制系统中应用越来越广泛,并且有着很大的应用前景,目前制冷与空调专业"PLC编程与应用技术"课程教学中教学形式单一,实验内容与专业脱节.本文以培养合格的面向工程应用和开发型人才为目的,从教学方法和教学内容上对课程进行探讨,结合项目教学方法,设计了与"PLC编程与应用技术"课程相配合的教学项目.项目教学点燃学生的求知欲和探索欲,提高学习兴趣,教学项目锻炼了学生分析和解决问题的能力,提高了学生制冷空调控制工程的实践能力,为培养制冷领域智能化人才奠定了坚实的基础.

The development of second-stage concentrators and lens systems facilitates the application of upward-facing cavities in solar energy utilization. To design a cavity with high efficiency and low cost, convective heat loss must be determined then controlled. Taking the commonly used isothermal cylindrical cavities as the object, natural convective heat loss characteristics were analysed numerically. The influences of surface temperature, tilt angle and aperture ratio were elaborated. Results show that the natural convective heat loss keeps increasing with increasing aperture ratio. As the tilt angle reduces, the natural convective heat loss increases before attaining a maximum at about-30 & DEG; after which it drops. In short, the natural convective heat loss is proportional to the air velocity, the higher the air velocity, the larger the heat loss. A strategy controlling the air velocity is thus proposed to control the convective heat loss. Unlike isoflux conditions, a clockwise vortex forms inside the cavity for the tilt angle less than-30 & DEG;. Large prediction errors are presented for using previous correlations to predict the natural convection heat loss of upward-facing cylindrical cavities. Hence, a novel empirical correlation based upon the influence characteristics of studied factors and with high accuracy is developed.

本文介绍了吸附式制冷系统吸附床传热传质强化的途径,分析比较了多种途径采用的方法.结果表明:吸附式制冷系统吸附床一般通过增加传热面积、加强颗粒间传热和降低接触热阻3种途径,采用翅片式、涂抹式和添加剂法等9种方法来提高吸附床的传热性能,强化传质的方法常用浸渍法;吸附床的传热面积通过增加翅片的方法来增大虽简单有效,但也增加翅片和吸附剂之间的热阻;涂抹式吸附床的传热性能和再生速度能够得到有效提高,但由于涂覆层厚度的局限增加了吸附床金属的质量并降低热容量比;固化法、添加法和化学复合法联合应用效果会更佳,添加硫酸处理过的膨胀石墨并进行固化的吸附剂,其导热率可达到未处理的150倍.

河南省是中原文化经济的中心,也是中国重要的农业粮食生产大省,又是中国重要的交通运输枢纽,其冷链仓储物流的形成和冷库发展对河南省乃至于全国的交通运输和现代农业养殖产业化的发展都具有着重要的意义和影响.本文围绕河南省冷库建设优势、建设现状及当前冷库存在的主要问题进行分析,并结合这些实际问题提出相关研究对策.以期对河南省冷库建设和冷链物流的发展有推动作用,为河南省的经济发展社会发展规划提供一定参考.

结合制冷与空调专业学生特点和教学现状,文章以丰富教学内容、改善教学方法、合理进行课堂设计等方面为切入点,探究制冷与空调专业英语课程在应用型本科高校中的教学改革.

本文从应用型本科的内涵、特点及能源动力类专业的人才标准入手,针对目前应用型本科院校能源动力类专业在人才培养方面,学生在学习、就业方面存在的问题进行分析,从而在课程体系改革、教师队伍建设、实践教学改革等方面提出了改善的途径和措施,为应用型本科能源动力类专业的建设和发展提供参考.