Search results for: transient temperature prediction

Authors: Li Kewen, Su Zhaoxin, Wang Xingmou, Zhu Jian Bing

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Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development.

Keywords: Convolutional neural network, lithology, prediction of reservoir lithology, seismic attributes.

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Authors: Chunshien Li, Jhao-Wun Hu, Tai-Wei Chiang, Tsunghan Wu

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Time series forecasting is an important and widely popular topic in the research of system modeling. This paper describes how to use the hybrid PSO-RLSE neuro-fuzzy learning approach to the problem of time series forecasting. The PSO algorithm is used to update the premise parameters of the proposed prediction system, and the RLSE is used to update the consequence parameters. Thanks to the hybrid learning (HL) approach for the neuro-fuzzy system, the prediction performance is excellent and the speed of learning convergence is much faster than other compared approaches. In the experiments, we use the well-known Mackey-Glass chaos time series. According to the experimental results, the prediction performance and accuracy in time series forecasting by the proposed approach is much better than other compared approaches, as shown in Table IV. Excellent prediction performance by the proposed approach has been observed.

Keywords: forecasting, hybrid learning (HL), Neuro-FuzzySystem (NFS), particle swarm optimization (PSO), recursiveleast-squares estimator (RLSE), time series

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Authors: P. Mottaghizadeh, M. Bagheri

Abstract:

In the present paper, the three-dimensional temperature field of tool is determined during the machining and compared with experimental work on C45 workpiece using carbide cutting tool inserts. During the metal cutting operations, high temperature is generated in the tool cutting edge which influence on the rate of tool wear. Temperature is most important characteristic of machining processes; since many parameters such as cutting speed, surface quality and cutting forces depend on the temperature and high temperatures can cause high mechanical stresses which lead to early tool wear and reduce tool life. Therefore, considerable attention is paid to determine tool temperatures. The experiments are carried out for dry and orthogonal machining condition. The results show that the increase of tool temperature depends on depth of cut and especially cutting speed in high range of cutting conditions.

Keywords: Finite element method, Machining, Temperature measurement, Thermal fields

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Authors: Li-Yeh Chuang, Yu-Jen Hou, Jr., Cheng-Hong Yang

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Single nucleotide polymorphisms (SNPs) hold much promise as a basis for disease-gene association. However, research is limited by the cost of genotyping the tremendous number of SNPs. Therefore, it is important to identify a small subset of informative SNPs, the so-called tag SNPs. This subset consists of selected SNPs of the genotypes, and accurately represents the rest of the SNPs. Furthermore, an effective evaluation method is needed to evaluate prediction accuracy of a set of tag SNPs. In this paper, a genetic algorithm (GA) is applied to tag SNP problems, and the K-nearest neighbor (K-NN) serves as a prediction method of tag SNP selection. The experimental data used was taken from the HapMap project; it consists of genotype data rather than haplotype data. The proposed method consistently identified tag SNPs with considerably better prediction accuracy than methods from the literature. At the same time, the number of tag SNPs identified was smaller than the number of tag SNPs in the other methods. The run time of the proposed method was much shorter than the run time of the SVM/STSA method when the same accuracy was reached.

Keywords: Genetic Algorithm (GA), Genotype, Single nucleotide polymorphism (SNP), tag SNPs.

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Authors: R. Oonsivilai, N. Chaijareonudomroung, Y. Huantanom, A. Oonsivilai

Abstract:

The optimal extraction condition of dried Echinocactus grusonii powder was studied. The three independent variables are raw material drying temperature, extraction temperature, and extraction time. The dependent variables are both yield percentage of crude extract and total phenolic quantification as gallic acid equivalent in crude extract. The experimental design was based on central composite design. Highest yield percentage of crude extract could get from extraction condition at raw material drying temperature at 60°C, extraction temperature at 15°C, and extraction time for 25 min °C. Moreover, the crude extract with highest phenolic occurred by extraction condition of raw material drying temperature at 60°C, extraction temperature at 35 °C, and extraction lasting 25 min.

Keywords: Drying temperature, Extraction temperature, Optimal condition, Total phenolic

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Authors: Akram Khaleghei Ghosheh Balagh, Viliam Makis

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In this paper, we investigate the residual life prediction problem for a partially observable system subject to two failure modes, namely a catastrophic failure and a failure due to the system degradation. The system is subject to condition monitoring and the degradation process is described by a hidden Markov model with unknown parameters. The parameter estimation procedure based on an EM algorithm is developed and the formulas for the conditional reliability function and the mean residual life are derived, illustrated by a numerical example.

Keywords: Partially observable system, hidden Markov model, competing risks, residual life prediction.

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Authors: Leonardo Juan Ramírez López, Javier Oswaldo Rodriguez Velasquez

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We have designed a Holter that measures the heart´s activity for over 24 hours, implemented a prediction methodology, and generate alarms as well as indicators to patients and treating physicians. Various diagnostic advances have been developed in clinical cardiology thanks to Holter implementation; however, their interpretation has largely been conditioned to clinical analysis and measurements adjusted to diverse population characteristics, thus turning it into a subjective examination. This, however, requires vast population studies to be validated that, in turn, have not achieved the ultimate goal: mortality prediction. Given this context, our Insight Research Group developed a mathematical methodology that assesses cardiac dynamics through entropy and probability, creating a numerical and geometrical attractor which allows quantifying the normalcy of chronic and acute disease as well as the evolution between such states, and our Tigum Research Group developed a holter device with 12 channels and advanced computer software. This has been shown in different contexts with 100% sensitivity and specificity results.

Keywords: Entropy, mathematical, prediction, cardiac, holter, attractor.

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Authors: Nebi Gedik

Abstract:

Regardless of age or gender, cardiovascular illnesses are a serious health concern because of things like poor eating habits, stress, a sedentary lifestyle, hard work schedules, alcohol use, and weight. It tends to happen suddenly and has a high rate of recurrence. Machine learning models can be implemented to assist healthcare systems in the accurate detection and diagnosis of cardiovascular disease (CVD) in patients. Improved heart failure prediction is one of the primary goals of researchers using the heart disease dataset. The purpose of this study is to identify the feature or features that offer the best classification prediction for CVD detection. The support vector machine classifier is used to compare each feature's performance. It has been determined which feature produces the best results.

Keywords: Cardiovascular disease, feature extraction, supervised learning, support vector machine.

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Authors: Wei-hua Ai, Wei Yan, Xiang Li

Abstract:

Data compression is used operationally to reduce bandwidth and storage requirements. An efficient method for achieving lossless weather radar data compression is presented. The characteristics of the data are taken into account and the optical linear prediction is used for the PPI images in the weather radar data in the proposed method. The next PPI image is identical to the current one and a dramatic reduction in source entropy is achieved by using the prediction algorithm. Some lossless compression methods are used to compress the predicted data. Experimental results show that for the weather radar data, the method proposed in this paper outperforms the other methods.

Keywords: Lossless compression, weather radar data, optical linear prediction, PPI image

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Authors: Rajeev Ranjan Sahay

Abstract:

Transient storage zones along the flow paths of rivers have great influence on the dispersion of pollutants that are either accidentally or otherwise led into them. The speed with which these pollution clouds get transported and dispersed downstream is, to a large extent, explained by the longitudinal dispersion coefficients in the free-flowing zones of rivers (Kf). In the present work, a new empirical expression for Kf has been derived employing genetic programming (GP) on published dispersion data. The proposed expression uses few hydraulic and geometric characteristics of a river that are readily available to field engineers. Based on various performance indices, the proposed expression is found superior to other existing expression for Kf.

Keywords: Dispersion, parameter estimation, rivers, transient pollutant.

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Authors: Abir Yahya, Hacen Dhahri, Khalifa Slimi

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The present paper deals with a numerical simulation of temperature field inside a solid oxide fuel cell (SOFC) components. The temperature distribution is investigated using a co-flow planar SOFC comprising the air and fuel channel and two-ceramic electrodes, anode and cathode, separated by a dense ceramic electrolyte. The Lattice Boltzmann method (LBM) is used for the numerical simulation of the physical problem. The effects of inlet temperature, anode thermal conductivity and current density on temperature distribution are discussed. It was found that temperature distribution is very sensitive to the inlet temperature and the current density.

Keywords: Solid oxide fuel cell, Heat sources, temperature, Lattice Boltzmann method.

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Authors: Kimberly Jane S. Uy, Patricia Angela Reyes-Abu, Wen Yaw Chung

Abstract:

A high precision temperature insensitive current and voltage reference generator is presented. It is specifically developed for temperature compensated oscillator. The circuit, designed using MXIC 0.5um CMOS technology, has an operating voltage that ranges from 2.6V to 5V and generates a voltage of 1.21V and a current of 6.38 ӴA. It exhibits a variation of ±0.3nA for the current reference and a stable output for voltage reference as the temperature is varied from 0°C to 70°C. The power supply rejection ratio obtained without any filtering capacitor at 100Hz and 10MHz is -30dB and -12dB respectively.

Keywords: Current reference, voltage reference, threshold voltage, temperature compensation, mobility.

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Authors: Jamalizadeh, M.R., Moghaddamnia, A., Piri, J., Arbabi, V., Homayounifar, M., Shahryari, A.

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Dust storms are one of the most costly and destructive events in many desert regions. They can cause massive damages both in natural environments and human lives. This paper is aimed at presenting a preliminary study on dust storms, as a major natural hazard in arid and semi-arid regions. As a case study, dust storm events occurred in Zabol city located in Sistan Region of Iran was analyzed to diagnose and predict dust storms. The identification and prediction of dust storm events could have significant impacts on damages reduction. Present models for this purpose are complicated and not appropriate for many areas with poor-data environments. The present study explores Gamma test for identifying inputs of ANNs model, for dust storm prediction. Results indicate that more attempts must be carried out concerning dust storms identification and segregate between various dust storm types.

Keywords: Dust Storm, Gamma Test, Prediction, ANNs, Zabol.

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Authors: S. Kapoor, R. Liu, X. J. Wu, M. X. Yao

Abstract:

A group of Stellite alloys are studied in consideration of temperature effects on their hardness and wear resistance. The hardness test is conducted on a micro-hardness tester with a hot stage equipped that allows heating the specimen up to 650°C. The wear resistance of each alloy is evaluated using a pin-on-disc tribometer with a heating furnace built-in that provides the temperature capacity up to 450°C. The experimental results demonstrate that the hardness and wear resistance of Stellite alloys behave differently at room temperature and at high temperatures. The wear resistance of Stellite alloys at room temperature mainly depends on their carbon content and also influenced by the tungsten content in the alloys. However, at high temperatures the wear mechanisms of Stellite alloys become more complex, involving multiple factors. The relationships between chemical composition, microstructure, hardness and wear resistance of these alloys are studied, with focus on temperature effect on these relations.

Keywords: Stellite alloy, temperature, hardness, wear resistance

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Authors: Indra Budi, Rizal Fathoni Aji, Agus Widodo

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Prediction of future research topics by using time series analysis either statistical or machine learning has been conducted previously by several researchers. Several methods have been proposed to combine the forecasting results into single forecast. These methods use fixed combination of individual forecast to get the final forecast result. In this paper, quite different approach is employed to select the forecasting methods, in which every point to forecast is calculated by using the best methods used by similar validation dataset. The dataset used in the experiment is time series derived from research report in Garuda, which is an online sites belongs to the Ministry of Education in Indonesia, over the past 20 years. The experimental result demonstrates that the proposed method may perform better compared to the fix combination of predictors. In addition, based on the prediction result, we can forecast emerging research topics for the next few years.

Keywords: Combination, emerging topics, ensemble, forecasting, machine learning, prediction, research topics, similarity measure, time series.

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Authors: Rami El-Hajj Mohamad, Mahmoud Skafi, Ali Massoud Haidar

Abstract:

Several meteorological parameters were used for the  prediction of monthly average daily global solar radiation on  horizontal using recurrent neural networks (RNNs). Climatological  data and measures, mainly air temperature, humidity, sunshine  duration, and wind speed between 1995 and 2007 were used to design  and validate a feed forward and recurrent neural network based  prediction systems. In this paper we present our reference system  based on a feed-forward multilayer perceptron (MLP) as well as the  proposed approach based on an RNN model. The obtained results  were promising and comparable to those obtained by other existing  empirical and neural models. The experimental results showed the  advantage of RNNs over simple MLPs when we deal with time series  solar radiation predictions based on daily climatological data.

Keywords: Recurrent Neural Networks, Global Solar Radiation, Multi-layer perceptron, gradient, Root Mean Square Error.

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Authors: Chang Su Woo, Hyun Sung Park

Abstract:

Useful lifetime evaluation of railpads were very important in design procedure to assure the safety and reliability. It is, therefore, necessary to establish a suitable criterion for the replacement period of rail pads. In this study, we performed properties and accelerated heat aging tests of rail pads considering degradation factors and all environmental conditions including operation, and then derived a lifetime prediction equation according to changes in hardness, thickness, and static spring constants in the Arrhenius plot to establish how to estimate the aging of rail pads. With the useful lifetime prediction equation, the lifetime of e-clip pads was 2.5 years when the change in hardness was 10% at 25°C; and that of f-clip pads was 1.7 years. When the change in thickness was 10%, the lifetime of e-clip pads and f-clip pads is 2.6 years respectively. The results obtained in this study to estimate the useful lifetime of rail pads for high speed trains can be used for determining the maintenance and replacement schedule for rail pads.

Keywords: Rail pads, accelerated test, Arrhenius plot, useful lifetime prediction.

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Authors: S. Wiriyarattanakun, A. Ruengsiriwatanakun, S. Noimanee

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The drainage Estimating is an important factor in dam management. In this paper, we use fuzzy support vector regression (FSVR) to predict the drainage of the Sirikrit Dam at Uttaradit province, Thailand. The results show that the FSVR is a suitable method in drainage estimating.

Keywords: Drainage Estimation, Prediction.

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Authors: Anne-Lena Kampen, Øivind Kure

Abstract:

Machine learning (ML) can be implemented in Wireless Sensor Networks (WSNs) as a central solution or distributed solution where the ML is embedded in the nodes. Embedding improves privacy and may reduce prediction delay. In addition, the number of transmissions is reduced. However, quality factors such as prediction accuracy, fault detection efficiency and coordinated control of the overall system suffer. Here, we discuss and highlight the trade-offs that should be considered when choosing between embedding and centralized ML, especially for multihop networks. In addition, we present estimations that demonstrate the energy trade-offs between embedded and centralized ML. Although the total network energy consumption is lower with central prediction, it makes the network more prone for partitioning due to the high forwarding load on the one-hop nodes. Moreover, the continuous improvements in the number of operations per joule for embedded devices will move the energy balance toward embedded prediction.

Keywords: Central ML, embedded machine learning, energy consumption, local ML, Wireless Sensor Networks, WSN.

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Authors: Parvinder S. Sandhu, Sandeep Khimta, Kiranpreet Kaur

Abstract:

The prediction of Software quality during development life cycle of software project helps the development organization to make efficient use of available resource to produce the product of highest quality. “Whether a module is faulty or not" approach can be used to predict quality of a software module. There are numbers of software quality prediction models described in the literature based upon genetic algorithms, artificial neural network and other data mining algorithms. One of the promising aspects for quality prediction is based on clustering techniques. Most quality prediction models that are based on clustering techniques make use of K-means, Mixture-of-Guassians, Self-Organizing Map, Neural Gas and fuzzy K-means algorithm for prediction. In all these techniques a predefined structure is required that is number of neurons or clusters should be known before we start clustering process. But in case of Growing Neural Gas there is no need of predetermining the quantity of neurons and the topology of the structure to be used and it starts with a minimal neurons structure that is incremented during training until it reaches a maximum number user defined limits for clusters. Hence, in this work we have used Growing Neural Gas as underlying cluster algorithm that produces the initial set of labeled cluster from training data set and thereafter this set of clusters is used to predict the quality of test data set of software modules. The best testing results shows 80% accuracy in evaluating the quality of software modules. Hence, the proposed technique can be used by programmers in evaluating the quality of modules during software development.

Keywords: Growing Neural Gas, data clustering, fault prediction.

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Authors: Jung-Ho Moon, Myung-Gon Yoon, Tae Kwon Ha

Abstract:

High temperature deformation behavior of cast 310S stainless steel has been investigated in this study by performing tensile and compression tests at temperatures from 900 to 1200^oC. Rectangular ingots of which the dimensions were 350×350×100 in millimeter were cast using vacuum induction melting. Phase equilibrium was calculated using the FactSage®, thermodynamic software and database. Thermal expansion coefficient was also measured on the ingot in the temperature range from room temperature to 1200^oC. Tensile strength of cast 310S stainless steel was 9 MPa at 1200^oC, which is a little higher than that of a wrought 310S. With temperature decreased, tensile strength increased rapidly and reached up to 72 MPa at 900^oC. Elongation also increased with temperature decreased. Microstructure observation revealed that s phase was precipitated along the grain boundary and within the matrix over 1200^oC, which is detrimental to high temperature elongation.

Keywords: Stainless steel, STS 310S, high temperature deformation, microstructure, mechanical properties.

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Authors: Edwaldo R. B. Monteiro, Patricia D. M. Plentz, Edson R. De Pieri

Abstract:

Mobile robotics is gaining an increasingly important role in modern society. Several potentially dangerous or laborious tasks for human are assigned to mobile robots, which are increasingly capable. Many of these tasks need to be performed within a specified period, i.e, meet a deadline. Missing the deadline can result in financial and/or material losses. Mechanisms for predicting the missing of deadlines are fundamental because corrective actions can be taken to avoid or minimize the losses resulting from missing the deadline. In this work we propose a simple but reliable deadline missing prediction mechanism for mobile robots through the use of historical data and we use the Pioneer 3-DX robot for experiments and simulations, one of the most popular robots in academia.

Keywords: Deadline missing, historical data, mobile robots, prediction mechanism.

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Authors: Hsin-Hung Chang, Jin-Lung Guan, Ming-Ta Yang

Abstract:

This paper presents an inexpensive and effective temperature-controlled chamber for temperature environment tests of Organic Light Emitting Diode (OLED) panels. The proposed chamber is a compact warmer and cooler with an exact temperature control system. In the temperature-controlled space of the chamber, thermoelectric modules (TEMs) are utilized to cool or to heat OLED panels, novel fixtures are designed to flexibly clamp the OLED panels of different size, and special connectors for wiring between the OLED panels and the test instrument are supplied. The proposed chamber has the following features. (1) The TEMs are solid semi-conductive devices, so they operate without noise and without pollution. (2) The volume of the temperature-controlled space of the chamber about 160mm*160mm*120mm, so the chamber are compact and easy to move. (3) The range of the controlled temperatures is from -10 oC to +80 oC, and the precision is ?0.5 oC. (4) The test instrument can conveniently and easily measure the OLED panels via the novel fixtures and special connectors. In addition to a constant temperature being maintained in the chamber, a temperature shock experiments can run for a long time. Therefore, the chamber will be convenient and useful for temperature environment tests of OLED panels.

Keywords: Thermoelectric module, Temperature environment test, OLED, chamber.

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Authors: Jiakai Li, Gursel Serpen, Steven Selman, Matt Franchetti, Mike Riesen, Cynthia Schneider

Abstract:

This paper presents the development of a Bayesian belief network classifier for prediction of graft status and survival period in renal transplantation using the patient profile information prior to the transplantation. The objective was to explore feasibility of developing a decision making tool for identifying the most suitable recipient among the candidate pool members. The dataset was compiled from the University of Toledo Medical Center Hospital patients as reported to the United Network Organ Sharing, and had 1228 patient records for the period covering 1987 through 2009. The Bayes net classifiers were developed using the Weka machine learning software workbench. Two separate classifiers were induced from the data set, one to predict the status of the graft as either failed or living, and a second classifier to predict the graft survival period. The classifier for graft status prediction performed very well with a prediction accuracy of 97.8% and true positive values of 0.967 and 0.988 for the living and failed classes, respectively. The second classifier to predict the graft survival period yielded a prediction accuracy of 68.2% and a true positive rate of 0.85 for the class representing those instances with kidneys failing during the first year following transplantation. Simulation results indicated that it is feasible to develop a successful Bayesian belief network classifier for prediction of graft status, but not the graft survival period, using the information in UNOS database.

Keywords: Bayesian network classifier, renal transplantation, graft survival period, United Network for Organ Sharing

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Authors: Mohamed Moustafa Mahmoud Sedky

Abstract:

In Line start permanent magnet synchronous motor,  eccentricity is a common fault that can make it necessary to remove  the motor from the production line. However, because the motor may  be inaccessible, diagnosing the fault is not easy. This paper presents  an FEM that identifies different models, static eccentricity, dynamic  eccentricity, and mixed eccentricity, at no load and full load. The  method overcomes the difficulty of applying FEMs to transient  behavior. It simulates motor speed, torque and flux density  distribution along the air gap for SE,DE, and ME. This paper  represents the various effects of different eccentricitiestypes on the  transient performance.

Keywords: Line Start Permanent magnet, synchronous machine, Static Eccentricity, Dynamic Eccentricity, Mixed Eccentricity.

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Authors: Joseph C. Chen, Venkata Mohan Kudapa

Abstract:

Development of two real-time surface roughness (Ra) prediction systems for milling operations was attempted. The systems used not only cutting parameters, such as feed rate and spindle speed, but also the cutting current generated and corrected by a clamp type energy sensor. Two different approaches were developed. First, a fuzzy inference system (FIS), in which the fuzzy logic rules are generated by experts in the milling processes, was used to conduct prediction modeling using current cutting data. Second, a neuro-fuzzy system (ANFIS) was explored. Neuro-fuzzy systems are adaptive techniques in which data are collected on the network, processed, and rules are generated by the system. The inference system then uses these rules to predict Ra as the output. Experimental results showed that the parameters of spindle speed, feed rate, depth of cut, and input current variation could predict Ra. These two systems enable the prediction of Ra during the milling operation with an average of 91.83% and 94.48% accuracy by FIS and ANFIS systems, respectively. Statistically, the ANFIS system provided better prediction accuracy than that of the FIS system.

Keywords: Surface roughness, input current, fuzzy logic, neuro-fuzzy, milling operations.

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Authors: Abhishek Swaroop, Awadhesh Kumar Singh

Abstract:

The group mutual exclusion (GME) problem is a variant of the mutual exclusion problem. In the present paper a token-based group mutual exclusion algorithm, capable of handling transient faults, is proposed. The algorithm uses the concept of dynamic request sets. A time out mechanism is used to detect the token loss; also, a distributed scheme is used to regenerate the token. The worst case message complexity of the algorithm is n+1. The maximum concurrency and forum switch complexity of the algorithm are n and min (n, m) respectively, where n is the number of processes and m is the number of groups. The algorithm also satisfies another desirable property called smooth admission. The scheme can also be adapted to handle the extended group mutual exclusion problem.

Keywords: Dynamic request sets, Fault tolerance, Smoothadmission, Transient faults.

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Authors: Vuk M. Popovic, Dunja D. Popovic

Abstract:

Software maintenance phase is started once a software project has been developed and delivered. After that, any modification to it corresponds to maintenance. Software maintenance involves modifications to keep a software project usable in a changed or a changing environment, to correct discovered faults, and modifications, and to improve performance or maintainability. Software maintenance and management of software maintenance are recognized as two most important and most expensive processes in a life of a software product. This research is basing the prediction of maintenance, on risks and time evaluation, and using them as data sets for working with neural networks. The aim of this paper is to provide support to project maintenance managers. They will be able to pass the issues planned for the next software-service-patch to the experts, for risk and working time evaluation, and afterward to put all data to neural networks in order to get software maintenance prediction. This process will lead to the more accurate prediction of the working hours needed for the software-service-patch, which will eventually lead to better planning of budget for the software maintenance projects.

Keywords: Laboratory information system, maintenance engineering, neural networks, software maintenance, software maintenance costs.

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Authors: Jamal S. Yassin

Abstract:

This paper is to formulate a mathematical model to predict the amounts of the emissions produced from the combustion process of the gas turbine unit of the jet engine. These emissions have bad impacts on the environment if they are out of standards, which cause real threats to all type of life on the earth. The amounts of the emissions from the gas turbine engine are functions to many operational and design factors. In landing-takeoff (LTO) these amounts are not the same as in taxi or cruise of the plane using jet engines, because of the difference in the activity period during these operating modes. These emissions can be affected by several physical and chemical variables, such as fuel type, fuel to air ratio or equivalence ratio, flame temperature, combustion pressure, in addition to some inlet conditions such as ambient temperature and air humidity. To study the influence of these variables on the amounts of these emissions during the combustion process in the gas turbine unit, a computer program has been developed by using the visual basic 6 software. Here, the analysis of the combustion process is carried out by considering it as a chemical reaction with shifting equilibrium to find the products of the combustion of the octane fuel, at different equivalence ratios, compressor pressure ratios (CPR) and combustion temperatures. The results obtained have shown that there is noticeable influence of the equivalence ratio, CPR, and the combustion temperature on the amounts of the main emissions which are considered pollutants, such as CO, CO₂ and NO.

Keywords: Mathematical model, gas turbine unit, equivalence ratio, emissions, shifting equilibrium.

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Authors: Na Li, Samuel Simon Araya, Søren Knudsen Kær

Abstract:

This study investigates the effects of operating parameters of different current density, temperature and pressure on the performance of a proton exchange membrane (PEM) water electrolysis stack. A 7-cell PEM water electrolysis stack was assembled and tested under different operation modules. The voltage change and polarization curves under different test conditions, namely current density, temperature and pressure, were recorded. Results show that higher temperature has positive effect on overall stack performance, where temperature of 80 ℃ improved the cell performance greatly. However, the cathode pressure and current density has little effect on stack performance.

Keywords: PEM electrolysis stack, current density, temperature, pressure.

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