بهبود تولید بیودیزل با کمک اولتراسونیک حاصل از ضایعات صنعت گوشت (چربی خوک) با استفاده از نانوکاتالیزور اکسید مس سبز: مقایسه سطح پاسخ و مدل سازی شبکه عصبی
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 25
سوخت زیستی سبز ، تمیز و پایدار تنها گزینه به منظور کاهش کابرد سوخت های فسیلی ، پاسخگویی به تقاضای زیاد انرژی و کاهش آلودگی هوا است. تولید بیودیزل زمانی ارزان می شود که از یک پیش ماده ارزان ، کاتالیزور سازگار با محیط زیست و فرآیند مناسب استفاده کنیم. پیه خوک از صنعت گوشت حاوی اسید چرب بالا است و به عنوان یک پیش ماده موثر برای تهیه بیودیزل کاربرد دارد. این مطالعه بیودیزل را از روغن پیه خوک از طریق فرآیند استری سازی دو مرحله ای با کمک اولتراسونیک و کاتالیزور تولید می کند. عصاره Cinnamomum tamala (C. tamala) برای تهیه نانوذرات CuO مورد استفاده قرار گرفت و با استفاده از طیف مادون قرمز ، پراش اشعه ایکس ، توزیع اندازه ذرات ، میکروسکوپ الکترونی روبشی و انتقال مشخص شد. تولید بیودیزل با استفاده از طرح Box-Behnken (BBD) و شبکه عصبی مصنوعی (ANN) ، در محدوده متغیرهای زمان اولتراسونیک (us )(20-40 min)، بارگیری نانوکاتالیزور 1-3) CuO درصد وزنی( ، و متانول به قبل از نسبت مولی PTO (10:1e30:1) مدلسازی شد. آنالیز آماری ثابت کرد که مدل سازی شبکه عصبی بهتر از BBD است. عملکرد بهینه 97.82٪ با استفاده از الگوریتم ژنتیک (GA) در زمان US: 35.36 دقیقه ، بار کاتالیزور CuO: 2.07 درصد وزنی و نسبت مولی: 29.87: 1 به دست آمد. مقایسه با مطالعات قبلی ثابت کرد که اولتراسونیک به میزان قابل توجهی موجب کاهش بار نانوکاتالیزور CuO می شود ، و نسبت مولی را افزایش می دهد و این فرایند را بهبود می بخشد.
کلمات کلیدی: چربی خوک | التراسونیک | اکسید مس | سنتز سبز | شبکه عصبی | سطح پاسخ
|مقاله ترجمه شده|
A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem
یک الگوریتم ژنتیک خودآموز مبتنی بر یادگیری تقویتی برای مسئله زمان بندی انعطاف پذیر مشاغل فروشگاهی -2020
As an important branch of production scheduling, flexible job-shop scheduling problem (FJSP) is difficult to solve and is proven to be NP-hard. Many intelligent algorithms have been proposed to solve FJSP, but their key parameters cannot be dynamically adjusted effectively during the calculation process, which causes the solution efficiency and quality not being able to meet the production requirements. Therefore, a self-learning genetic algorithm (SLGA) is proposed in this paper, in which genetic algorithm (GA) is adopted as the basic optimization method and its key parameters are intelligently adjusted based on reinforcement learning (RL). Firstly, the selflearning model is analyzed and constructed in SLGA, SARSA algorithm and Q-Learning algorithm are applied as the learning methods at initial and later stages of optimization, respectively, and the conversion condition is designed. Secondly, the state determination method and reward method are designed for RL in GA environment. Finally, the learning effect and performance of SLGA in solving FJSP are compared with other algorithms using two groups of benchmark data instances with different scales. Experiment results show that the proposed SLGA significantly outperforms its competitors in solving FJSP.
Keywords: Flexible job-shop scheduling problem (FJSP) | Self-learning genetic algorithm (SLGA) | Genetic algorithm (GA) | Reinforcement learning (RL)
Application of optimized Artificial and Radial Basis neural networks by using modified Genetic Algorithm on discharge coefficient prediction of modified labyrinth side weir with two and four cycles
استفاده از شبکه های عصبی بهینه سازی شده مصنوعی و شعاعی با استفاده از الگوریتم ژنتیک اصلاح شده بر پیش بینی ضریب تخلیه ریزگرد سمت اصلاح شده با دو و چهار چرخه-2020
Determining the discharge coefficient is one of the most important processes in designing side weirs. In this study, the structure of Artificial Neural Network (ANN) and Radial Basis Neural Network (RBNN) methods are optimized by a modified Genetic Algorithm (GA). So two new hybrid methods of Genetic Algorithm Artificial neural network (GAA) and Genetic Algorithm Radial Basis neural network (GARB), were introduced and compared with each other. The modified GA was used to find the neuron number in the hidden layers of the ANN and to find the spread value and the neuron number of the RBNN method, as well. GAA and GARB were tested for predicting the discharge coefficient of a modified labyrinth side weir he GARB method could successfully predict the accurate discharge coefficient even in cases where there is a limited number of train datasets available.
Keywords: Artificial neural network | Discharge coefficient | Hybrid model | Labyrinth side weir | Modified | Genetic algorithm | Radial basis neural network
Research on BP network for retrieving extinction coefficient from Mie scattering signal of lidar
تحقیقات بر روی شبکه BP برای بازیابی ضریب خاموشی از سیگنال پراکندگی میای LIDAR-2020
Mie lidar is a powerful tool for detecting the optical properties of atmospheric aerosols. However, there are two unknown parameters in the Mie lidar equation: the extinction coefficient and the backscattering coefficient. In the common methods for solving the equation, it is necessary to make assumptions about the relationship between the two unknown parameters. These assumptions will reduce the detection precision of extinction coefficient. In view of this, the back propagation (BP) neural network is used to retrieve extinction coefficient from the Mie scattering signal of lidar. Firstly, the structure and main parameters of the BP network are designed according to the practical application. In order to improve the convergence speed and prevent falling into local minima, the initial weights and thresholds of BP network are optimized by genetic algorithm (GA). Then the GA-BP network is trained with Mie scattering signal and the extinction coefficient retrieved by Raman method. Thus the mathematical relationship between Mie scattering signal and the extinction coefficient is stored in the BP network. The trained GA-BP network is then used to retrieve the extinction coefficient from Mie scattering signal in different conditions and the applicability of the GA-BP network is researched. The research will promote the development of Mie lidar retrieving algorithm.
Keywords: Aerosol | Mie scattering | Lidar | Extinction coefficient | BP network | Genetic algorithm
Genetic state-grouping algorithm for deep reinforcement learning
الگوریتم گروه بندی حالت ژنتیکی برای یادگیری تقویتی عمیق-2020
Although Reinforcement learning has already been considered one of the most important and wellknown techniques of machine learning, its applicability remains limited in the real-world problems due to its long initial learning time and unstable learning. Especially, the problem of an overwhelming number of the branching factors under real-time constraint still stays unconquered, demanding a new method for the next generation of reinforcement learning. In this paper, we propose Genetic State- Grouping Algorithm based on deep reinforcement learning. The core idea is to divide the entire set of states into a few state groups. Each group consists of states that are mutually similar, thus representing their common features. The state groups are then processed with the Genetic Optimizer, which finds outstanding actions. These steps help the Deep Q Network avoid excessive exploration, thereby contributing to the significant reduction of initial learning time. The experiment on the real-time fighting video game (FightingICE) shows the effectiveness of our proposed approach.
Keywords: Reinforcement learning | Genetic algorithm | Hybrid method | Monte Carlo Tree Search | Game AI
Energy management of hybrid electric vehicles: A review of energy optimization of fuel cell hybrid power system based on genetic algorithm
مدیریت انرژی وسایل نقلیه الکتریکی هیبریدی: مروری بر بهینه سازی انرژی سیستم قدرت هیبریدی سلول سوختی بر اساس الگوریتم ژنتیکی-2020
Under the background of current environmental pollution and serious shortage of fossil energy, the development of electric vehicles driven by clean new energy is the key to solve this problem, especially the hybrid electric vehicle driven by fuel cell is the most effective solution. Many scholars have found that the output performance of hybrid system is an important reason to determine the life of fuel cell. Unreasonable output will affect the control characteristics of the drive system, resulting in a series of serious consequences such as the reduction of the life of fuel cell hybrid power system. Therefore, the energy management strategy and performance optimization of hybrid system is the key to ensure the normal operation of the system. At present, many excellent researchers have carried out relevant research in this field. Genetic algorithm is a heuristic algorithm, which has better optimization performance. It can easily choose satisfactory solutions according to the optimization objectives, and make up for these shortcomings by using its own characteristics. These characteristics make genetic algorithm have outstanding advantages in the iterative optimization of energy management strategy. This paper analyzes and summarizes the optimization effect of genetic algorithm in various energy management strategies, aiming to analyze and select the optimization rules and parameters, optimization objects and optimization objectives. This paper hopes to provide guidance for the optimal control strategy and structural design of the fuel cell hybrid power system, contribute to the research on improving the energy utilization efficiency of the hybrid power system and extending the life of the fuel cell, and provide more ideas for the optimization of energy management in the future.
Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Hybrid power system | Genetic algorithm |Optimization parameters and objectives
Prediction of Disc Cutter Life during Shield Tunneling with AI via the Incorporation of a Genetic Algorithm into a GMDH-Type Neural Network
پیش بینی عمر برش دیسک در حین تونل سازی سپر با هوش مصنوعی از طریق ادغام الگوریتم ژنتیک در شبکه عصبی نوع GMDH-2020
Disc cutter consumption is a critical problem that influences work performance during shield tunneling processes and directly affects the cutter change decision. This study proposes a new model to estimate the disc cutter life (Hf) by integrating a group method of data handling (GMDH)-type neural network (NN) with a genetic algorithm (GA). The efficiency and effectiveness of the GMDH network structure are optimized by the GA, which enables each neuron to search for its optimum connections set from the previous layer. With the proposed model, monitoring data including the shield performance database, disc cutter consumption, geological conditions, and operational parameters can be analyzed. To verify the performance of the proposed model, a case study in China is presented and a database is adopted to illustrate the excellence of the hybrid model. The results indicate that the hybrid model predicts disc cutter life with high accuracy. The sensitivity analysis reveals that the penetration rate (PR) has a significant influence on disc cutter life. The results of this study can be beneficial in both the planning and construction stages of shield tunneling.
Keywords: Disc cutter life | Shield tunneling | Operational parameters | GMDH–GA
Optimization based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle considering fuel economy and fuel cell lifespan
استراتژی مدیریت انرژی مبتنی بر بهینه سازی موتور سوخت هیبریدی / باتری / ماورا بنفش با توجه به مصرف سوخت و طول عمر سلول سوختی-2020
Optimization of energy management strategy (EMS) for fuel cell/battery/ultracapacitor hybrid electrical vehicle (FCHEV) is primarily aimed on reducing fuel consumption. However, serious power fluctuation has effect on the durability of fuel cell, which still remains one challenging barrier for FCHEVs. In this paper, we propose an optimized frequency decoupling EMS using fuzzy control method to extend fuel cell lifespan and improve fuel economy for FCHEV. In the proposed EMS, fuel cell, battery and ultracapacitor are employed to supply low, middle and high-frequency components of required power, respectively. For accurately adjusting membership functions of proposed fuzzy controllers, genetic algorithm (GA) is adopted to optimize them considering multiple constraints on fuel cell power fluctuation and hydrogen consumption. The proposed EMS is verified by Advisor-Simulink and experiment bench. Simulation and experimental results confirm that the proposed EMS can effectively reduce hydrogen consumption in three typical drive cycles, limit fuel cell power fluctuation within 300 W/s and thus extend fuel cell lifespan.
Keywords: Fuel cell electrical hybrid vehicle | Energy management strategy | Frequency decoupling | Fuzzy control | Genetic algorithm
A genetic Artificial Bee Colony algorithm for signal reconstruction based big data optimization
یک الگوریتم ژنتیکی زنبورعسل مصنوعی برای بهینه سازی داده های بزرگ مبتنی بر بازسازی سیگنال-2020
In recent years, the researchers have witnessed the changes or transformations driven by the existence of the big data on the definitions, complexities and future directions of the real world optimization problems. Analyzing the capabilities of the previously introduced techniques, determining possible drawbacks of them and developing new methods by taking into consideration of the unique properties related with the big data are nowadays in urgent demands. Artificial Bee Colony (ABC) algorithm inspired by the clever foraging behaviors of the real honey bees is one of the most successful swarm intelligence based optimization algorithms. In this study, a novel ABC algorithm based big data optimization technique was proposed. For exploring the solving abilities of the proposed technique, a set of experimental studies has been carried out by using different signal decomposition based big data optimization problems presented at the Congress on Evolutionary Computation (CEC) 2015 Big Data Optimization Competition. The results obtained from the experimental studies first were compared with the well-known variants of the standard ABC algorithm named gbest-guided ABC (GABC), ABC/best/1, ABC/best/2, crossover ABC (CABC), converge-onlookers ABC (COABC) and quick ABC (qABC). The results of the proposed ABC algorithm were also compared with the Differential Evolution (DE) algorithm, Genetic algorithm (GA), Firefly algorithm (FA), Fireworks algorithm (FW), Phase Base Optimization (PBO) algorithm, Particle Swarm Optimization (PSO) algorithm and Dragonfly algorithm (DA) based big data optimization techniques. From the experimental studies, it was understood that the newly introduced ABC algorithm based technique is capable of producing better or at least promising results compared to the mentioned big data optimization techniques for all of the benchmark instances.
Keywords: Big data optimization | Signal decomposition | Artificial Bee Colony
Optimal design of a university campus micro-grid operating under unreliable grid considering PV and battery storage
طراحی بهینه یک میکرو شبکه دانشگاه که تحت شبکه غیرقابل اعتماد با توجه به ذخیره سازی PV و باتری کار می کند-2020
This paper proposes a novel methodology for redesigning a micro-grid characterized by a heavy reliance on diesel generators due to receiving power supply from an unreliable grid. The new design aims at phasing out the diesel generators and replacing them with a hybrid energy system composed of photovoltaics and a battery storage system. Two optimization approaches are adopted, a heuristic genetic algorithm approach is used to achieve sub-optimal sizing of the hybrid system sources and a rules-based dynamic programming approach to ensure optimal power flow. In order to reduce the computation time, a novel combinational approach employing genetic algorithm, dynamic programming and rules-based algorithm is proposed. The intervention of the dynamic programming for optimal power flow is restricted to certain active hours within a given day, while the rules-based power flow algorithm runs only outside those hours. The study demonstrates that the application of the hybrid system yields minimal operational cost by almost entirely phasing out the diesel generators and significantly reducing the energy purchased from the grid during peak hours. The micro-grid of a university campus is used as a case study where energy and economic indicators are derived to prove the superiority of the proposed techniques.
Keywords: Microgrid optimal design | Energy management system | Genetic algorithm | Dynamic programming | Energy economics