Optimization Of Production Systems Research Articles

Optimization of Renewable Energy Hydrogen Production Systems Using Volatility Improved Multi-Objective Particle Swarm Algorithm

Optimizing the energy structure to effectively enhance the integration level of renewable energy is an important pathway for achieving dual carbon goals. This study utilizes an improved multi-objective particle swarm optimization algorithm based on load fluctuation rates to optimize the architecture and unit capacity of hydrogen production systems. It investigates the optimal configuration methods for the architectural model of new energy hydrogen production systems in Xining City, Qinghai Province, as well as the internal storage battery, ALK hydrogen production equipment, and PEM hydrogen production equipment, aiming at various scenarios of power sources such as wind, solar, wind–solar complementary, and wind–solar–storage complementary, as well as intermittent hydrogen production scenarios such as hydrogen stations, hydrogen metallurgy, and continuous hydrogen production scenarios such as hydrogen methanol production. The results indicate that the fluctuation of hydrogen load scenarios has a significant impact on the installed capacity and initial investment of the system. Compared with the single-channel photovoltaic hydrogen production scheme, the dual-channel hydrogen production scheme still reduces equipment capacity by 6.04% and initial investment by 6.16% in the chemical hydrogen scenario with the least load fluctuation.

Design and operation optimization of an on-site hydrogen production and purification integration system for PEMFC

Proton exchange membrane fuel cell (PEMFC) is a promising green and efficient power generation unit using high-purity hydrogen. A high-purity hydrogen supply is significant for the PEMFC. However, previous studies on hydrogen generation for PEMFC mainly focus on simulation, small-scale hydrogen production, or the production and purification in separation. In the present study, an on-site hydrogen supply system with methanol steam reforming and membrane separator is designed and constructed with the goal of providing high-purity hydrogen for a 200 W scale PEMFC. The operation performance of the integrated system is experimentally evaluated, and the influences of critical parameters on hydrogen production are investigated. The response surface method (RSM) is employed to analyze the interaction effect of factors on the hydrogen production rate and optimize the operating parameters. The experimental results show that the integrated system can continuously and stably supply high-purity hydrogen with CO concentration below 10 ppm. The maximum attainable hydrogen flow obtained by RSM is 1460.33 ml/min, which is 24.32% higher than that achieved in the original experiment. The results hold significant implications for revealing the operational characteristics of the integrated system and optimizing the operating parameters.

INTEGER LINEAR PROGRAMMING MODEL FOR AGGREGATE PRODUCTION PLANNING, APPLICATION OF LEARNING CURVES, AND HYPOTHESIS TESTING

ABSTRACT: Efficient production management and business administration are essential to maintain competitiveness in a business environment in constant change and complexity; the optimization of production systems becomes imperative to minimize costs and maximize operational efficiency. In this context, the implementation of advanced integer linear programming techniques emerges as a solution for aggregate production planning, based on the establishment of a uniform flow of processes to contribute to improving the efficiency of the system and reducing associated cost overruns. This article presents the analysis of the use of integer linear programming for aggregate production planning, applied to the production process of concrete blocks and concrete for structural masonry in the company Blocks del Sinú SAS in the department of Córdoba - Colombia. An optimization model is proposed that satisfies the forecasted demand through adjustments to the levels of production, labor, inventories, contracting and raw materials. The results show that with the standardization of the production process and work methods to minimize the costs of the production plan and meet the demand requirements in accordance with the company's capacity, demand will strictly occur within the planning horizon. , which implies that there will be no shortage of raw materials. Keywords: Aggregate planning, integer linear programming, production.

Potential imbalanced differences of grain production in the sustainable development of county cities—a case study of Jiangsu Province

IntroductionGrain production at the regional level is related to the strategic development orientation of the country as a whole, and maintaining the stability of grain production is of great importance in ensuring the sustainable development of the food system. Studying the potential imbalance differences resulting from the spatial and temporal development of grain production and its driving factors will help to reduce the contradiction between the rapid development of the urban economy and the inappropriate use of arable land resources, thus coordinating regional agricultural development and further clarifying the direction and focus of future food security construction work.MethodsThis study focuses on the potential imbalance differences based on spatial and temporal development of grain production in Jiangsu Province during the period 1990–2020, and explores the spatial and temporal patterns and driving mechanisms of grain production spatial autocorrelation model and spatial error model are applied to analysis the spatial and temporal characteristics of grain production and security in Jiangsu Province and their influencing factors.ResultsThe results are as follows: (1) The total amount of food in Jiangsu Province increased by 17.36% during the 30-year period, but the growth rate was lower than the national average. (2) Regional differences are significant, with a significant decline in grain production in southern Jiangsu and growth in northern and central Jiangsu. (3) The center of gravity for grain production has shifted to northern Jiangsu. (4) Grain production is significantly correlated with socio-economic factors such as per capita ownership, sown area, irrigated area, and cultivated area.DiscussionTo ensure the sustainability of food production in the future, attention should be paid to the risk of imbalance in regional food production, coordination of socio-economic factors, promotion of efficient agricultural production, improvement of the relevant policy system and optimization of the food production system.

Techno-economic optimization of a photovoltaic-wind energy-based hydrogen production system: A case study of different cities of Saudi Arabia

Techno-economic optimization of a photovoltaic-wind energy-based hydrogen production system: A case study of different cities of Saudi Arabia

Multi-objective optimization of production system with staggered production

In the manufacturing industry, timely order fulfilment from diverse customers is important for operational profitability, given the constraints of limited production capacity. This paper proposes a novel queueing system for a production system, in which order processing times are categorised as regular or rush based on customer preference. The proposed queueing system employs variable service rates and service strategies in conjunction with staggered production to deal with heterogeneous arrivals. We compute the steady-state probability distribution and develop system performance indicators. The lower and upper bounds of the expected sojourn time for rush and regular orders are derived. Numerical results show that an appropriate switching policy can reduce rush orders by approximately 35% while regular orders increase by less than 10%, demonstrating significant improvement in order fulfilment management. We formulate a multi-objective optimisation problem and implement the NSGA-II algorithm to obtain Pareto optimal solutions. Finally, three regression models are proposed to determine the minimum cost and the associated optimal service rates, given the maximum acceptable value of the expected number of rush orders. The established models can explain at least 80% of the dataset of the optimal solutions and thus our numerical findings provide useful guidance for decision-makers and production managers.

Energetic Valorization of the Innovative Building Envelope: An Overview of Electric Production System Optimization

The world population increased from 1 billion in 1800 to around 8 billion today. The Population Division of the United Nations predicts a global population of approximately 10.4 billion people by the end of the century. That represents over 2 billion more people. Moreover, the global community is currently experiencing a precarious state due to the enduring repercussions of the COVID-19 pandemic across all sectors, including energy. Given the rising global population and the limited availability of primary energy resources, we must reach a balance between the demands of a growing human population and the planet’s carrying capacity. The dreadful conflict in Ukraine has precipitated an enormous energy crisis. This crisis has served as a warning to the world population of how much it depends on this resource to survive. In France, the building sectors, specifically residential and tertiary, account for 45% of the total final energy consumption. It is the first energy consumer of the country and one of the most polluting (i.e., about 34% of CO2 emitted by France). Consequently, we must consider alternative energy resource forms (i.e., substitution energy forms). Harvesting energy from the building envelope may be a viable technique for partially satisfying the electricity demands of building users. In this context, scientific research offers considerable potential for developing more innovative and efficient systems. This article aims to review the state-of-the-art of advances on the subject to orient and further optimize energy production systems, particularly electricity. This work addresses several points of view: it discusses the overall backdrop of the present study and introduces the subject; details the research strategy and procedures used to produce this paper; develops the state-of-the-art on the potential for generating or recovering power from the building envelope; presents the SWOT analysis of the earlier-described systems. Finally, it concludes by offering findings and viewpoints.

Real-time optimization of large-scale hydrogen production systems using off-grid renewable energy: Scheduling strategy based on deep reinforcement learning

Aiming to amplify the renewable energy consumption capacity, this study delineates the development of an off-grid Renewable Energy Large-Scale Hydrogen Production System (H2-RES). The system was optimized for economic efficiency and safety, promising a reduction in both the investment cost for grid connection and the overall cost of hydrogen production from electrolytic water. We presented a comprehensive mathematical model for each H2-RES unit and designed a control strategy to enhance energy optimization and management. An intelligent energy scheduling policy empowered by the DDPG algorithm is introduced for optimal decision-making in continuous state and action spaces. Comparative analysis with traditional control policies, PSO, and DQN algorithms underscores the superior economic efficiency, enhanced renewable energy consumption, and safe operation facilitated by DDPG. These findings underscore the academic and engineering potential of DDPG in the energy dispatch of H2-RES.

Optimization Method of Production System for Coalbed Methane Wells throughout Life Cycle

Different from conventional gas reservoirs, the permeability of coalbeds is affected by stress sensitivity and matrix shrinkage during production. These two conditions lead to lower permeability in the reservoir and affect the production efficiency of the gas well. In addition, coalbed methane wells have single-phase water flow in the initial stage of production. When the reservoir pressure is reduced to its critical desorption pressure, the adsorbed gas in the reservoir desorbs into the pore space and participates in the flow. The flow state in the reservoir changes from single-phase to two phase, and the permeability of the reservoir decreases. The occurrence of these three damage mechanisms is related to the flow rate of fluid in the reservoir. At present, there is a lack of research on the optimization of drainage and production systems considering the damage mechanism of coal reservoirs. This study comprehensively considers how to optimize the production rate of coal reservoirs under the influence of stress sensitivity, matrix shrinkage and two phase flow in the production process to achieve the purpose of production with the least damage to the reservoir.

Parametric Study and Optimization of Hydrogen Production Systems Based on Solar/Wind Hybrid Renewable Energies: A Case Study in Kuqa, China

Based on the concept of sustainable development, to promote the development and application of renewable energy and enhance the capacity of renewable energy consumption, this paper studies the design and optimization of renewable energy hydrogen production systems. For this paper, six different scenarios for grid-connected and off-grid renewable energy hydrogen production systems were designed and analyzed economically and technically, and the optimal grid-connected and off-grid systems were selected. Subsequently, the optimal system solution was optimized by analyzing the impact of the load data and component capacity on the grid dependency of the grid-connected hydrogen production system and the excess power rate of the off-grid hydrogen production system. Based on the simulation results, the most matched load data and component capacity of different systems after optimization were determined. The grid-supplied power of the optimized grid-connected hydrogen production system decreased by 3347 kWh, and the excess power rate of the off-grid hydrogen production system decreased from 38.6% to 10.3%, resulting in a significant improvement in the technical and economic performance of the system.

A comprehensive analysis and multi-objective optimization of a sustainable production system based on renewable energies

The adoption and promotion of clean and sustainable energy sources bring about significant benefits to the environment, economy, and society, both in the present and for future generations. This study introduces a pioneering solar-wind biomass multi-generation system, aimed at facilitating the sustainable production of electricity, fresh water, hydrogen, and heating. This system provides a comprehensive evaluation of the influence of environmental conditions and design factors on system efficiency, taking into account the crucial aspects of energy, exergy, economy, and environment (4E). Additionally, a meticulous sensitivity analysis is conducted to identify and prioritize key decision variables that exert the most significant impact on the overall system. Ultimately, the multi-objective optimization is conducted by taking into account various priorities in four distinct scenarios. This approach enables the simultaneous or individual optimization of the system's useful objective functions in relation to the total cost rate. Subsequently, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is employed to determine the ideal point that best fulfills the optimization criteria. By employing TOPSIS, this study aims to provide a robust and systematic decision-making framework for scenario selection in various domains. By evaluating the optimal points across various scenarios, the first scenario yields the most favorable outcomes in terms of the system's useful outputs. These include an exergy efficiency of 19.63 %, hydrogen production of 11.07 kg/h, fresh water production of 34.16 kg/s, and a total cost rate amounting to 213.80 $/h. Additionally, a considerable decrease of 22.65 % in the total cost rate is noted at the optimal point.

Research on design and multi-frequency scheduling optimization method for flexible green ammonia system

As an important chemical product and energy carrier, green ammonia is a way to consume large quantities of green hydrogen produced from renewable electricity. For the optimization of the green ammonia production systems, a primary problem is to solve the electricity-hydrogen-ammonia matching problem under multi-frequency and multi-amplitude load fluctuations. In this work, a method for capacity designing and scheduling optimization of a hybrid multi-frequency flexible green ammonia system is constructed. The method can be applied to design the whole green ammonia system in grid-connected and isolated scenarios. The established model can accurately describe the electricity-hydrogen-ammonia matching characteristics of each module. Furthermore, many important practical engineering constraints are taken into account, such as the operating time of the chemical device, and the flexibility of the ammonia device. This work verifies the effectiveness of the proposed method based on a real case, and the impact of load range, adjustment speed, and adjustment frequency of the ammonia synthesis process on the product cost and module capacity.

Layered power scheduling optimization of PV hydrogen production system considering performance attenuation of PEMEL

To analyze the additional cost caused by the performance attenuation of a proton exchange membrane electrolyzer (PEMEL) under the fluctuating input of renewable energy, this study proposes an optimization method for power scheduling in hydrogen production systems under the scenario of photovoltaic (PV) electrolysis of water. First, voltage and performance attenuation models of the PEMEL are proposed, and the degradation cost of the electrolyzer under a fluctuating input is considered. Then, the calculation of the investment and operating costs of the hydrogen production system for a typical day is based on the life cycle cost. Finally, a layered power scheduling optimization method is proposed to reasonably distribute the power of the electrolyzer and energy storage system in a hydrogen production system. In the up-layer optimization, the PV power absorbed by the hydrogen production system was optimized using MALTAB+Gurobi. In low-layer optimization, the power allocation between the PEMEL and battery energy storage system (BESS) is optimized using a non-dominated sorting genetic algorithm (NSGA-II) combined with the firefly algorithm (FA). A better optimization result, characterized by lower degradation and total costs, was obtained using the method proposed in this study. The improved algorithm can search for a better population and obtain optimization results in fewer iterations. As a calculation example, data from a PV power station in northwest China were used for optimization, and the effectiveness and rationality of the proposed optimization method were verified.

Development technologies for Triassic Chang 7 shale oil in Ordos Basin: A case study of Qingcheng Oilfield, NW China

The reservoirs in the seventh member of the Triassic Yanchang Formation (Chang 7 Member) in the Qingcheng Oilfield of the Ordos Basin are characterized by thin sandbody, tight rocks, high heterogeneity, low formation pressure coefficient, and complex geomorphology. Through the efforts in the stages of exploration, appraisal, pilot testing and development, a series of key technologies have been formed, including “sweet spot” optimization, differentiated three-dimensional well deployment, fast drilling and completion of large-cluster horizontal well, intensively-staged volume fracturing in long horizontal well, and optimization of rational production system. Furthermore, a production organization mode represented by factory-like operations on loess platform has been implemented. Application of these technologies has enabled to significantly improve the single-well production of the Qingcheng Oilfield, reduce the investment cost, and realize a large-scale and beneficial development at a full cost below $55 per barrel. In 2022, the annual production of Chang 7 shale oil in the Ordos Basin reached 221×104 t, accounting for 70% of the annual shale oil production of China. The practice of development technologies in the Qingcheng Oilfield provides valuable references for efficient development of continental shale oil.

49 Leveraging Big Data Analytics to Improve Disease Surveillance and Welfare in Pig Production

Abstract Large amounts of data are routinely collected in pig production, including operational farm data and, more recently, data from digital tools such as remote and on-site sensing technologies. Furthermore, additional sources of information, such as demographic, economic, and weather variables, can be combined with farm data. Integrating and analyzing such data can generate valuable insights and data-driven decision-making tools for disease surveillance, animal welfare, and production system optimization. This presentation discusses two examples of big data integration and analysis in the context of optimizing management practices in pig production. The first example pertains to a data analytics project related to total transportation losses (TTL) of market-weight pigs, an important animal welfare concern that economically impacts producers and abattoirs. After merging and checking data for errors, the final dataset included 4.5M+ pigs from 420 farms loaded in 26,819 shipments delivered to two abattoirs. Results from a generalized additive mixed model (GAMM) technique showed that the risk of loss per shipment was constant for temperature-humidity index (THI) values between −10 and 10. Additionally, a strong interaction between wind speed and precipitation was associated with TTL. TTL were strongly associated with the type of driver and the distance traveled, in addition to weather variables. The second example pertains to swine disease surveillance, in which data from a multi-site system, where pigs are transported between farms after each particular production phase, were investigated. Reports of 76,566 shipments across sites were obtained and used to characterize pig movement flow, conduct percolation analysis to investigate network robustness to interventions for diseases with different transmissibility, and assess the potential impact of each farm type on disease dissemination across the system. Topological properties of networks were investigated, such as the number of nodes and edges, degree assortativity, density, average path length, diameter, clustering coefficients, giant strongly connected component, giant weakly connected component, giant in component, and giant out component. Overall, the results indicated that gilt development units, nursery, and sow farms have a more central role in the pig production hierarchical structure. As such, they are potential major factors in the introduction and spread of diseases in the system. Wean-to-finishing and finishing sites displayed high in-degree values, indicating that they are more susceptible to infection. Additionally, the characteristics of a disease should have strong implications for biosecurity practices across production sites.

System Dynamics Model of Decentralized Household Electricity Storage Implementation: Case Study of Latvia

Increasing renewable energy share in total energy production is a direction that leads toward the European Union’s aims of carbon neutrality by 2050, as well as increasing energy self-sufficiency and independence. Some of the main challenges to increasing renewable energy share while providing an efficient and secure energy supply are related to the optimization and profitability of de-centralized energy production systems. Integration of energy storage systems in addition to decentralized renewable energy production, for example, by solar panels, leads to more effective electricity supply and smart energy solutions. The modeling of such a complex dynamic system can be performed using the system dynamics method. The main aim of this research is to build and validate the basic structure of the system dynamics model for PV and battery diffusion in the household sector. A system dynamics model predicting the implementation of battery storage in private households was created for the case study of Latvia. Modeling results reveal that under the right conditions for electricity price and investment costs and with the right policy interventions, battery storage technologies combined with PV panels have a high potential for utilization in the household sector. Model results show that in a baseline scenario with no additional policies, up to 21,422 households or 10.8% of Latvian households could have combined PV and battery systems installed in 2050. Moderate subsidy policy can help to increase this number up to 25,118.

Analysis of zootechnical and economic indicators in carp and tilapia production

Tilapia has been consolidated as a widely produced and widespread species throughout Brazil, in the south of the country, due to the low temperatures during the winter, more easily acclimatized species have been chosen, such as carp. There are still no studies comparing the economic viability of these two consolidated activities. In this context, the present study carried out an analysis of zootechnical and economic indicators of the production of Carps (Ctenopharyngodon idella) and (Hypophthalmichthys nobilis) and Nile Tilapia (Oreochromis niloticus), in a family property, in Rio Grande do Sul. The results showed that both activities presented satisfactory economic viability. Carp showed a profitability index per year of 16.28% and tilapia of 16.18%, values ​​higher than the Selic rate in Brazil of 13.25% per year. The cost of feeding tilapia represents 73% of production costs, followed by labor costs (9%) and fingerlings (7%). In carp production, the cost of food was 43%, followed by labor (36%) and fingerlings (5%). It is concluded that there is a possibility of increasing profitability in the next production cycles, based on the optimization of production systems.

Production system efficiency optimization through application of a hybrid artificial intelligence solution

ABSTRACT Industry 4.0 seeks waste reduction via the optimization of production systems integrating technology and process. In addition to evaluating existing methods and technologies, academia also develops new ones. This research proposes a new hybrid artificial intelligence (AI) solution for production system efficiency optimization that combines data envelopment analysis (DEA), machine learning (ML)-based simulation and genetic algorithms (GAs) using real-world sensor data from a thermoelectric power plant. In the proposed method, DEA is employed to identify the production system’s efficient frontier, which is used to build an ML model that predicts production efficiency through simulation. A genetic algorithm is then utilized to propose those settings that result in optimized production efficiency. Although the possibility of combining DEA-ML and ML-GA has been discussed in the literature, no research was found that combines these three methods for production efficiency optimization. The proposed solution was tested and validated using real-world data. The benefits of the hybrid AI solution were measured by comparing its predicted efficiency with the efficiencies achieved by running production with conventional control-loops based control systems. The results show that considerable efficiency improvement can be achieved using the proposed hybrid AI solution.

Use of the best-worst method to evaluate determinants affecting cost reduction of heat exchanger components in the Chinese automotive industry

As China is a major automobile manufacturing and consumption country, cost reduction is an important indicator for the operation of the automobile industry. There are many automobile-related components and parts, and each component is important to the full functionality of the automobile. This article focuses on automobiles’ heat exchanger components (HECs) because they have important connections with the engine, transmission, body, and hydraulic systems of automobiles. Since the HECs are important functional parts of automobiles, it is worthwhile to investigate the determinants affecting the cost reduction of HECs in the automobile industry. Hence, the main purpose of this article is to evaluate the determinants affecting the cost reduction of HECs in the Chinese automotive industry using the best-worst method (BWM). Firstly, based on the literature and the opinions of industry and expert interviews, five evaluation dimensions and 20 evaluation factors were developed. Secondly, an empirical survey was conducted using the BWM and expert questionnaires. Finally, the study’s results revealed that: (1) “Research and development” is the most critical evaluation dimension that affects the cost reduction of HECs in the Chinese automotive industry; (2) The determinants affecting the cost reduction of HECs in the Chinese automotive industry were “value analysis/value engineering,”“production system optimization,”“developing industry standards,”“lightweighting,”“synchronous new product development,” and “waste reduction.” The empirical findings showed that the first three determinants were particularly important in the decision-making factors of cost reduction. In addition, this article discusses and suggests these determinants as a reference for future cost reduction evaluation of HECs in the Chinese automotive industry.

Optimisation of size and control strategy in utility-scale green hydrogen production systems

The optimisation of green hydrogen production systems is challenging. Moreover, an accurate simulation of the system is required for effective optimisation. This study presents a novel method for optimising utility-scale hybrid photovoltaic–wind systems for hydrogen production using accurate simulation models. The optimisation objective is to minimise the levelised cost of hydrogen (LCOH) using genetic algorithms. Different types of systems (such as islanded systems, grid-connected systems with or without the possibility of purchasing electricity from the grid, and grid-connected systems considering power curtailment), are evaluated and optimised. Each combination of components and control strategy is simulated during the system lifetime (20 yrs) in time steps of 5 min, considering the degradation of renewable generators during the system lifetime and different real-time pricing curves and renewable resource curves for each year of the system lifetime. Accurate models are used in the simulations, including electrolyser efficiency dependent on the input power and cold-start extra ageing. An application example located in Zaragoza (Spain) is shown, obtaining LCOH from 4.74 to 16.06 €/kg, depending on the type of project and electrolyser.