1,426 research outputs found
Artificial neural networks and physical modeling for determination of baseline consumption of CHP plants
An effective modeling technique is proposed for determining baseline energy consumption in the industry.
A CHP plant is considered in the study that was subjected to a retrofit, which consisted of the implementation
of some energy-saving measures. This study aims to recreate the post-retrofit energy consumption
and production of the system in case it would be operating in its past configuration (before retrofit) i.e., the
current consumption and production in the event that no energy-saving measures had been implemented.
Two different modeling methodologies are applied to the CHP plant: thermodynamic modeling and artificial
neural networks (ANN). Satisfactory results are obtained with both modeling techniques. Acceptable
accuracy levels of prediction are detected, confirming good capability of the models for predicting plant
behavior and their suitability for baseline energy consumption determining purposes. High level of robustness
is observed for ANN against uncertainty affecting measured values of variables used as input in the
models. The study demonstrates ANN great potential for assessing baseline consumption in energyintensive
industry. Application of ANN technique would also help to overcome the limited availability of
on-shelf thermodynamic software for modeling all specific typologies of existing industrial processes
A Review of using Data Mining Techniques in Power Plants
Data mining techniques and their applications have developed rapidly during the last two decades. This paper reviews application of data mining techniques in power systems, specially in power plants, through a survey of literature between the year 2000 and 2015. Keyword indices, articles’ abstracts and conclusions were used to classify more than 86 articles about application of data mining in power plants, from many academic journals and research centers. Because this paper concerns about application of data mining in power plants; the paper started by providing a brief introduction about data mining and power systems to give the reader better vision about these two different disciplines. This paper presents a comprehensive survey of the collected articles and classifies them according to three categories: the used techniques, the problem and the application area. From this review we found that data mining techniques (classification, regression, clustering and association rules) could be used to solve many types of problems in power plants, like predicting the amount of generated power, failure prediction, failure diagnosis, failure detection and many others. Also there is no standard technique that could be used for a specific problem. Application of data mining in power plants is a rich research area and still needs more exploration
An Intelligent Monitoring Interface for a Coal-Fired Power Plant Boiler Trips
A power plant monitoring system embedded with artificial intelligence can enhance its effectiveness by reducing the time spent in trip analysis and follow up procedures. Experimental results showed that Multilayered perceptron neural network trained with Levenberg-Marquardt (LM) algorithm achieved the least mean squared error of 0.0223 with the misclassification rate of 7.435% for the 10 simulated trip prediction. The proposed method can identify abnormality of operational parameters at the confident level of ±6.3%
Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines
153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale
Mengenal pasti tahap motivasi dan kecenderungan keusahawanan di kalangan pelajar semester akhir Ijazah Sarjana Muda Kejuruteraan Mekanikal : satu tinjauan di KUiTTHO
Keusahawanan merupakan bidang yang mencabar dan tidak ramai yang mahu
menceburi bidang ini tenitaraa lulusan bidang kejnriiteraan. Mereka ini mempunyai
elemen keusahawanan nntuk menjadi usahawan yang beijaya. Oleh itu, timbul
persoalan mengapakah pelajar-pelajar ini kurang berminat dengan bidang
keusahawanan?. Bersesuaian dengan pennasalahan tersebut, kajian ini dijalankan
bertujuan untnk meninjau tahap motivasi bagi memberikan pendedalian dan menarik
kecenderungan pelajar kejuruteraan terliadap bidang keusahawanan. Objektif kajian
ini berdasarkan kepada beberapa aspek iaitn taliap pengetahuan keusahawanan,
motivasi, kecenderungan terhadap bidang keusahawanan dan keperluan subjek atau
elemen keusahawanan dalam jumsan kejuruteraan. Kajian yang telah dijalankan
adalah kajian deskriptif berbentuk tinjauan yang menggunakan soal selidik sebagai
instrumen untuk mendapatkan data. Dengan menggimakan persampelan rawak
mudah, seramai 99 orang pelajar semester akhir ijazah Saijana Muda Kejuruteraan
Mekanikal telah dipilih sebagai responden kajian. Instrumen kajian adalah soal
selidik dan data-data yang diperolehi telah dianalisis menggimakan perisian
Statistical Package For Social Science versi 11.0 untuk mendapatkan nilai min dan
peratus. Hasil kajian mendapati bahawa pelajar-pelajai' ini mempunyai
kecenderungan terhadap bidang keusahawanan dengan skor min keseluruhan 3.573
dan taliap motivasi keusahawanan yang tinggi dengan skor min keseluaihan 3.965
tetapi kekurangan pengetahuan dalam bidang keusahawanan dengan skor min 3.16.
Oleh itu. adalah perlu elemen-elemen keusahawanan diterapkan ke dalam kuriknlum
kursus kejuaiteraan
Evaluation of combined heat and power (CHP) systems using fuzzy shannon entropy and fuzzy TOPSIS
Combined heat and power (CHP) or cogeneration can play a strategic role in addressing environmental issues and climate change. CHP systems require less fuel than separate heat and power systems in order to produce the same amount of energy saving primary energy, improving the security of the supply. Because less fuel is combusted, greenhouse gas emissions and other air pollutants are reduced. If we are to consider the CHP system as "sustainable", we must include in its assessment not only energetic performance but also environmental and economic aspects, presenting a multicriteria issue. The purpose of the paper is to apply a fuzzy multicriteria methodology to the assessment of five CHP commercial technologies. Specifically, the combination of the fuzzy Shannon's entropy and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach will be tested for this purpose. Shannon's entropy concept, using interval data such as the α-cut, is a particularly suitable technique for assigning weights to criteria — it does not require a decision-making (DM) to assign a weight to the criteria. To rank the proposed alternatives, a fuzzy TOPSIS method has been applied. It is based on the principle that the chosen alternative should be as close as possible to the positive ideal solution and be as far as possible from the negative ideal solution. The proposed approach provides a useful technical–scientific decision-making tool that can effectively support, in a consistent and transparent way, the assessment of various CHP technologies from a sustainable point of view
FUZZY MODEL OF THE OPERATIONAL POTENTIAL CONSUMPTION PROCESS OF A COMPLEX TECHNICAL SYSTEM
During the operation process of a system its technical state is changed. The changes take place because of the wearing factors impact. The impact depends on the flow and intensity of the operation process what is characterized by the time histories of the working parameters. Simultaneously, the changes of the technical state are correlated with the changes of the amount of the operational potential included in a system. In order to avoid the inability state occurrence the amount of this potential should be higher than the boundary value. The amount of the operational potential included in a system is determined by the values of the cardinal features of it but in the case of the real technical system the values cannot always be measured. Therefore, the amount of the operational potential and the technical state of the system cannot always be determined online. To solve this problem the model of the operational potential consumption process was created and presented in the paper. The model uses artificial intelligence techniques to calculate the change of the operational potential amount by determining the changes of the cardinal features of the system on the basis of the time histories of the working parameters. The verification of the model quality was performed using the pulverized boiler OP-650k-040 operating in the power plant. The description of the conducted research and the results of the verification were presented in the end of the paper proving the adequacy of the model implementation in the case of industrial objects
Corporation robots
Nowadays, various robots are built to perform multiple tasks. Multiple robots working
together to perform a single task becomes important. One of the key elements for multiple
robots to work together is the robot need to able to follow another robot. This project is
mainly concerned on the design and construction of the robots that can follow line. In this
project, focuses on building line following robots leader and slave. Both of these robots will
follow the line and carry load. A Single robot has a limitation on handle load capacity such as
cannot handle heavy load and cannot handle long size load. To overcome this limitation an
easier way is to have a groups of mobile robots working together to accomplish an aim that
no single robot can do alon
Application of Intelligent Computational Techniques in Power Plants:A Review
Growing worldwide demand for energy leads to increasing the levels of challenge in power plants management. These challenges include but are not limited to complex equipment maintenance, power estimation under uncertainty, and energy optimisation. Therefore, efficient power plant management is required to increase the power plant’s operational efficiency. Conventional optimisation tools in power plants are not reliable as it is challenging to monitor, model and analyse individual and combined components within power systems in a plant. However, intelligent computational tools such as artificial neural networks (ANN), nature-inspired computations and meta-heuristics are becoming more reliable, offering a better understanding of the behaviour of the power systems, which eventually leads to better energy efficiency. This paper aims to provide an overview of the development and application of intelligent computational tools such as ANN in managing power plants. Also, to present several applications of intelligent computational tools in power plants operations management. The literature review technique is used to demonstrate intelligent computational tools in various power plants applications. The reviewed literature shows that ANN has the greatest potential to be the most reliable power plant management tool
- …