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|
پیش بینی قیمت پایانی سهام با استفاده از روشهای یادگیری ماشینی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 12
پیش بینی دقیق بازگشت های بازار سهام به دلیل ماهیت فرّار و غیرخطی بازارهای مالی بورس، کاری بسیار چالش انگیز است. اثبات شده است که با معرفی هوش مصنوعی و قابلیت های محاسباتی افزون، روشهای برنامه ریزی شده برای پیش بینی بورس در پیش بینی قیمت سهام کارآمدتر هستند. در این کار تحقیقی، از روشهای شبکه عصبی مصنوعی و جنگل تصادفی برای پیش بینی قیمت نهایی روز بعدی برای شرکتهای متعلق به بخشهای کاری مختلف استفاده شده است. از داده های مالی مربوط به قیمت های باز، بالا، پایین و نهایی سهام برای خلق متغیرهای جدیدی استفاده می شود که این متغیرها به عنوان ورودی های مدل به کار می روند. مدلها با استفاده از شاخص های راهبردی استاندارد RMSE و MAPE ارزیابی می شوند. مقادیر پایین این دو شاخص نشان می دهد که این مدلها در پیش بینی قیمت نهایی سهام کارآمد هستند.
کلیدواژه ها: رگراسیون جنگل تصادفی | شبکه عصبی مصنوعی | پیش بینی بازار سهام
|مقاله ترجمه شده|
ANN modelling of CO2 refrigerant cooling system COP in a smart warehouse
مدل سازی ANN سیستم خنک کننده کولر CO2 COP در یک انبار هوشمند-2020
Industrial cooling systems consume large quantities of energy with highly variable power demand. To reduce environmental impact and overall energy consumption, and to stabilize the power requirements, it is recommended to recover surplus heat, store energy, and integrate renewable energy production. To control these operations continuously in a complex energy system, an intelligent energy management system can be employed using operational data and machine learning. In this work, we have developed an artificial neural network based technique for modelling operational CO2 refrigerant based industrial cooling systems for embedding in an overall energy management system. The operating temperature and pressure measurements, as well as the operating frequency of compressors, are used in developing operational model of the cooling system, which outputs electrical consumption and refrigerant mass flow without the need for additional physical measurements. The presented model is superior to a generalized theoretical model, as it learns from data that includes individual compressor type characteristics. The results show that the presented approach is relatively precise with a Mean Average Percentage Error (MAPE) as low as 5%, using low resolution and asynchronous data from a case study system. The developed model is also tested in a laboratory setting, where MAPE is shown to be as low as 1.8%.
Keywords: Industrial cooling systems | Carbon dioxide refrigerant | Artificial neural networks | Coefficient of performance | Energy storage | Smart warehouse
AI-based Framework for Deep Learning Applications in Grinding
چارچوبی مبتنی بر هوش مصنوعی برای کاربردهای یادگیری عمیق در شبکه سازی-2020
Rejection costs for a finish-machined gearwheel with grinding burn can rise to the order of 10,000 euros each. A reduction in costs by reducing rejection rate by only 5-10 pieces per year already amortizes costs for data-acquisition hardware for online process monitoring. The grinding wheel wear, one of the major influencing factors responsible for the grinding burn, depends on a large number of influencing variables like cooling lubricant, feed rate, circumferential wheel speed and wheel topography. In the past, machine learning algorithms such as Support Vector Machines (SVM), Hidden Markov Models (HMM) and Artificial Neural Networks (ANN) have proven effective for the predictive analysis of process quality. In addition to predictive analysis, AI-based applications for process control may raise the resilience of machining processes. Using machine learning methods may also lead to a heavy reduction of cost amassed due to a physical inspection of each workpiece. With this contribution, information from previous works is leveraged and an AI-based framework for adaptive process control of a cylindrical grinding process is introduced. For the development of such a framework, three research objectives have been derived: First, the dynamic wheel wear needs to be modelled and measured, because of its strong impact on the resulting workpiece quality. Second, models to predict the quality features of the produced workpieces depending on process setup parameters and materials used have to be established. Here, special focus is set on deriving models that are independent of a specific wheel-workpiece-pair. The opportunity to use such a model in a variety of grinding configurations gives the production line consistent process support. Third, the resilience of analytical models regarding graceful degradation of sensors needs to be tackled, since the stability of such systems has to be guaranteed to be used in productive environments. Process resilience against human errors and sensor failures leads to a minimization of rejection costs in production. To do so, a framework is presented, where virtual sensors, upon the failure or detection of an erroneous signal from physical sensors, will be activated and provide signals to the downstream smart systems until the process is completed or the physical sensor is changed.
Keywords: Cylindrical Grinding | Wheel Wear | Virtual Sensors | Process Resilience | Artificial Intelligence
A different sleep apnea classification system with neural network based on the acceleration signals
یک سیستم طبقه بندی sleep apnea متفاوت با شبکه عصبی مبتنی بر سیگنال های شتاب-2020
Background and objective: The apnea syndrome is characterized by an abnormal breath pause or reduction in the airflow during sleep. It is reported in the literature that it affects 2% of middle-aged women and 4% of middle-aged men, approximately. This study has vital importance, especially for the elderly, the disabled, and pediatric sleep apnea patients. Methods: In this study, a new diagnostic method is developed to detect the apnea event by using a microelectromechanical system (MEMS) based acceleration sensor. It records the value of acceleration by measuring the movements of the diaphragm in three axes during the respiratory. The measurements are carried out simultaneously, a medical spirometer (Fukuda Sangyo), to test the validity of measurement results. An artificial neural network model was designed to determine the apnea event. For the number of neurons in the hidden layer, 1-3-5-10-18-20-25 values were tried, and the network with three hidden neurons giving the most suitable result was selected. In the designed ANN, three layers were formed that three neurons in the hidden layer, the two neurons at the input, and two neurons at the output layer. Results: A study group was formed of 5 patients (having different characteristics (age, height, and body weight)). The patients in the study group have sleep apnea (SA) in different grades. Several 12.723 acceleration data (ACC) in the XYZ-axis from 5 different patients are recorded for apnea event training and detection. The measured accelerometer (ACC) data from one of the patients (called H1) are used to train an ANN. During the training phase, MSE is used to calculate the fitness value of the apnea event. Then Apnea event is detected successfully for the other patients by using ANN trained only with H1’s ACC data. Conclusions: The sleep apnea event detection system is presented by using ANN from directly acceleration values. Measurements are performed by the MEMS-based accelerometer and Industrial Spirometer simultaneously. A total of 12723 acceleration data is measured from 5 different patients. The best result in 7000 iterations was reached (the number of iterations was tried up to 10.000 with 1000 steps). 605 data of only H1 measurements are used to train ANN, and then all data used to check the performance of the ANN as well as H2, H3, H4, and H5 measurement results. MSE performance benchmark shows us that trained ANN successfully detects apnea events. One of the contributions of this study to literature is that only ACC data are used in the ANN training step. After training for one patient, the ANN system can monitor the apnea event situation on-line for others.
Keywords: Sleep apnea | Acceleration sensor | Acceleration data | Artificial neural network | Medical decision making
Modeling of forward osmosis process using artificial neural networks (ANN) to predict the permeate flux
مدل سازی فرآیند اسمزوز رو به جلو با استفاده از شبکه های عصبی مصنوعی (ANN) برای پیش بینی شار نفوذ-2020
Artificial neural networks (ANN) are black box models that are becoming more popular than transport-based models due to their high accuracy and less computational time in predictions. The literature shows a lack of ANN models to evaluate the forward osmosis (FO) process performance. Therefore, in this study, a multi-layered neural network model is developed to predict the permeate flux in forward osmosis. The developed model is tested for its generalization capability by including lab-scale experimental data from several published studies. Nine input variables are considered including membrane type, the orientation of membrane, molarity of feed solution and draw solution, type of feed solution and draw solution, crossflow velocity of the feed solution, and the draw solution and temperature of the feed solution and the draw solution. The development of optimum network architecture is supported by studying the impact of the number of neurons and hidden layers on the neural network performance. The optimum trained network shows a high R2 value of 97.3% that is the efficiency of the model to predict the targeted output. Furthermore, the validation and generalized prediction capability of the model is tested against untrained published data. The performance of the ANN model is compared with a transport-based model in the literature. A simple machine learning technique such as a multiple linear regression (MLR) model is also applied in a similar manner to be compared with the ANN model. ANN demonstrates its ability to form a complex relationship between inputs and output better than MLR.
Keywords: Artificial neural network | Forward osmosis | Water treatment | Desalination | Machine learning
Correlation minimizing replay memory in temporal-difference reinforcement learning
حداقل سازی همبستگی پاسخ در یادگیری تقویتی متفاوت موقت -2020
Online reinforcement learning agents are now able to process an increasing amount of data which makes their approximation and compression into value functions a more demanding task. To improve approx- imation, thus the learning process itself, it has been proposed to select randomly a mini-batch of the past experiences that are stored in the replay memory buffer to be replayed at each learning step. In this work, we present an algorithm that classifies and samples the experiences into separate contextual memory buffers using an unsupervised learning technique. This allows each new experience to be as- sociated to a mini-batch of the past experiences that are not from the same contextual buffer as the current one, thus further reducing the correlation between experiences. Experimental results show that the correlation minimizing sampling improves over Q-learning algorithms with uniform sampling, and that a significant improvement can be observed when coupled with the sampling methods that prioritize on the experience temporal difference error.
Keywords: Reinforcement learning | Temporal-difference learning | Replay memory | Artificial neural networks
Development of a chemometric-assisted spectrophotometric method for quantitative simultaneous determination of Amlodipine and Valsartan in commercial tablet
توسعه یک روش اسپکتروفتومتری با کمک شیمیایی برای تعیین کمی همزمان آملودیپین و والرسارتان در قرص تجاری-2020
In this study, two drugs named Amlodipine (AML) and Valsartan (VAL) related to the high blood pressure were simultaneously determined in synthetic mixtures and Valzomix tablet. For this purpose, the chemometric-assisted spectrophotometric method was developed without any prepreparation. Artificial intelligence techniques, including artificial neural network (ANN) and least squares support vector machine (LS-SVM) as chemometrics procedures were proposed. Feed-forward back-propagation neural network (FFBP-NN) with two different algorithms, containing Levenberg–Marquardt (LM) and gradient descent with momentum and adaptive learning rate backpropagation (GDX) was applied. To select the best model, several layers and neurons were investigated. The results revealed that layer = 5 with 6 neurons and layer = 2 with 10 neurons had lower mean square error (MSE) (1.41 × 10−24, 1.16 × 10-23) for AML and VAL, respectively. In the LS-SVM method, gamma (γ) and sigma (σ) parameters were optimized. γ and σ were obtained 50, 30 and 40, 40 with the root mean square error (RMSE) of 0.4290 and 0.5598 for AML and VAL, respectively. Analysis of the pharmaceutical formulation was evaluated through the chemometrics methods and high-performance liquid chromatography (HPLC) as a reference technique. The obtained results were statistically compared with each other using the one-way analysis of variance (ANOVA) test. There were no significant differences between them and the proposed method was satisfactory for estimating the components of the Valzomix tablet.
Keywords: Spectrophotometry | Amlodipine | Valsartan | Artificial neural network | Least squares support vector machine
Wake modeling of wind turbines using machine learning
مدل سازی توربین های بادی با استفاده از یادگیری ماشین-2020
In the paper, a novel framework that employs the machine learning and CFD (computational fluid dynamics) simulation to develop new wake velocity and turbulence models with high accuracy and good efficiency is proposed to improve the turbine wake predictions. An ANN (artificial neural network) model based on the backpropagation (BP) algorithm is designed to build the underlying spatial relationship between the inflow conditions and the three-dimensional wake flows. To save the computational cost, a reduced-order turbine model ADM-R (actuator disk model with rotation), is incorporated into RANS (Reynolds-averaged Navier-Stokes equations) simulations coupled with a modified k − ε turbulence model to provide big datasets of wake flow for training, testing, and validation of the ANN model. The numerical framework of RANS/ADM-R simulations is validated by a standalone Vestas V80 2MW wind turbine and NTNU wind tunnel test of double aligned turbines. In the ANN-based wake model, the inflow wind speed and turbulence intensity at hub height are selected as input variables, while the spatial velocity deficit and added turbulence kinetic energy (TKE) in wake field are taken as output variables. The ANN-based wake model is first deployed to a standalone turbine, and then the spatial wake characteristics and power generation of an aligned 8-turbine row as representation of Horns Rev wind farm are also validated against Large Eddy Simulations (LES) and field measurement. The results of ANNbased wake model show good agreement with the numerical simulations and measurement data, indicating that the ANN is capable of establishing the complex spatial relationship between inflow conditions and the wake flows. The machine learning techniques can remarkably improve the accuracy and efficiency of wake predictions.
Keywords: Wind turbine wake | Wake model | Artificial neural network (ANN) | Machine learning | ADM-R (actuator-disk model with rotation) | model | Computational fluid dynamics (CFD)
Analytical study on use of AI techniques in tourism sector for smarter customer experience management
مطالعه تحلیلی در مورد استفاده از تکنیک های هوش مصنوعی در بخش گردشگری برای مدیریت دقیقتر تجربه مشتری-2020
Artificial Intelligence is the new prime factor for paradigm shift of the new age technologies. It has created a new realm in every field- from education to entertainment or from biotechnology to manufacturing industry. Though tourism is a late runner in this race, but this sector has also witnessed a huge change with the magical touch of AI. This sector being one of the highly emerging sectors, contributing very high GDP , has adapted several machine learning techniques or data analytics, which has made tourism model smarter and dynamic. In India , tourism has an ample scope to grow and Indian tourism sectors are also adapting several popular AI techniques like deep learning, Artificial neural network, predictive analytics, robotics or new technologies like virtual reality or augmented reality. This technological adaptation has made their services much better, heled in dynamic pricing, or for smart customer experience management. This paper has conducted a study on Indian tourism sectors providing online services and discusses about the current AI technologies used by them while exploring the pros and cons faced by them . The paper is alienated in three different segments- section 1 contains introduction part, section 2 discusses about related works in similar area, third section deliberates about different AI techniques adapted by Indian tourism sectors along with their pro and cons.
Keywords : ChatBot | Artificial neural network | Machine Learning | Robotics | Predictive Analytics | Recommendation System