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Emission monitoring systems using Artificial Neural Networks
Get PDFThe problems of global warming and air pollution have led to enforcement of stringent constraints by governments and international community on the release of emissions. These constraints are now a days among the most important factors impacting plant operations. Inferential sensing techniques have been gaining momentum recently as viable alternatives to hardware sensors (i.e. Continuous Emission Monitoring System) in various situations. The core of inferential sensing is built on modeling and estimation techniques. This thesis work investigated and developed an inferential sensing technique for process emission monitoring using artificial neural networks. Neural Networks are powerful tool for modeling highly complex non-linear systems, especially when the physics of the systems is not clearly understood or difficult to determine as with emission from processes. Three different neural networks techniques were employed to model emission from the furnace unit of an industrial boiler. Multilayer Perceptron Neural Network was first investigated to construct the desired model; a trade-off was struck between the accuracy and the computational complexity of the model. Due to the drawbacks of multilayer perceptron neural network in getting trapped in local solution and it sensitivities to initial value of weights and biases other approaches were investigated. Two models were developed with two different sets of data using Radial Basis Function Network so that adequate and parsimonious model can be obtained with no local minimum problem. While a third technique was improvised to seize the merits of Neural Networks and Particle Swarm Optimization to deal with approximation embedded in the estimation of the d erivative in the multilayer perceptron thereby leading to a more accurate model. On a final note, real plant's data were archived from an industrial process that produces ample amount of emission; the data were used to develop a model for the process using the modeling techniques highlighted above. The results obtained clearly shows the ability of neural network techniques in developing process emission model
Efficient Test Compaction for Combinational Circuits Based on Fault Detection Count-Directed Clustering
Get PDFTest compaction is an effective technique for reducing test data volume and test application time. In this paper, we present a new static test compaction algorithm based on test vector decomposition and clustering. Test vectors are decomposed and clustered in an increasing order of faults detection count. This clustering order gives more degree of freedom and results in better compaction. Experimental results demonstrate the effectiveness of the proposed approach in achieving higher compaction in a much more efficient CPU time than previous clustering-based test compaction approaches
Merging GF(p) Elliptic Curve Point Adding and Doubling on Pipelined VLSI Cryptographic ASIC Architecture
Get PDFThis paper merges between elliptic curve addition presents a modified processor architecture for Elliptic Curve Cryptography computations in Galois Fields GF(p). The architecture incorporates the methodology of pipelining to utilize the benefit of both parallel and serial implementations. It allows the exploitation of the inherited independency that exists in elliptic curve point addition and doubling operations using a single pipelined core. The processor architecture showed attraction because of its improvement over many parallel and serial implementations of elliptic curve crypto-systems. It proved to be efficient having better performance with regard to area, speed, and power consumption
Evaluating Parallel Simulated Evolution Strategies for VLSI Cell Placement
Get PDFSimulated Evolution (SimE) is an evolutionary metaheuristic that has produced results comparable to well established stochastic heuristics such as SA, TS and GA, with shorter runtimes. However, for problems with a very large set of elements to optimize, such as in VLSI placement and routing, runtimes can still be very large and parallelization is an attractive option. Compared to other metaheuristics, parallelization of SimE has not been extensively explored. This paper presents a comprehensive set of parallelization approaches for SimE when applied to multiobjective VLSI cell placement problem. Each of these approaches are evaluated with respect to SimE characteristics and the constraints imposed by the problem instance. Conclusions drawn can be extended to parallelization of other SimE based optimization problems
Failure-Rate Prediction for De Havilland Dash-8 Tires Employing Neural-Network Technique
Get PDFAn artificial neural-network model for predicting the failure rate of De Havilland Dash-8 airplane tires utilizing the two-layered feedforward back-propagation algorithm as a learning rule is developed. The inputs to the neural network are independent variables, and the output is the failure rate of the tires. Six years of data are used for model building and validation. Model validation, which reflects the suitability of the model for future prediction, is performed by comparing the predictions of the model with that of theWeibull regression model. The results show that the failure rate predicted by the artificial neural network more closely agrees with the actual data than the failure rate predicted by the Weibull model
"Recurrence Formula For The Impulse Response Coefficients Of The SincN FIR Filter"
Get PDFA simple recurrence formula for computing the impulse response coefficients of the SincN FIR made of a cascade of N Sinc filters each of length M is presented. The formula allows for a much lower mathematical operation compared to the previously published format and thus leads to a faster execution time and simpler circuitry
"Development of Authentic Annual and Seasonal Lightning Thunderstorms Day Maps for Saudi Arabia"
Get PDFA new average annual thunder day map for Saudi Arabia is presented. Based on this map, the distribution of thunderstorms over Saudi Arabia is analysed in terms of the factors related to the lightning performance of transmission lines such as thunderstorm days per year (Td/yr). Lightning activity continues for the present to be represented by thunderstorm frequency, which is routinely recorded at meteorological observation sites. Thunderstorm occurrence at a particular location is usually expressed as the number of days in a calendar year when thunder was heard, averaged over several years. This paper examines thunderstorm days in different areas of Saudi Arabia and specifically those areas where lightning strikes are more frequent; for this purpose, the software ArcGIS is used to produce contour maps which demonstrate areas of concern in Saudi Arabia in the period 1985–2003. Establishing the annual and seasonal Td/yr for Saudi Arabia enables transmission and distribution line engineers to calculate and better design a lightning protection system. Maps of thunder days/year (Td/yr) were constructed on the basis of the database records available on lightning incidence in Saudi Arabia at the Presidency of Meteorology and Environment (PME) (http://www.pme.gov.sa/). Annual thunderstorms are most frequent over the southwestern parts of the country, and generally decrease towards the west and east. Due to its low latitude and less temporal change, the west coast of the Red Sea recorded the lowest Td/yr. A secondary maximum Td/yr is apparent in the southeast to central part of the country. Thunderstorm frequency does not, in general, appear to vary in any consistent way with rainfall. There appears to be no evidence of any widespread temporal trend in thunderstorm frequency. The southern region in general, and especially the cities of Abha, Taif and Al-Baha, has shown greater numbers of thunderstorm days all year round. Similarly, this variation did show higher frequency throughout the year. The development of lightning incidence and the counting of Td/yr, as well as the establishment of annual and seasonal lightning maps of Saudi Arabia, are initiating a new era of producing and archiving thunderstorm maps and data records which serve the PME, the utilities, industry and the publi
Multiobjective Evolutionary Algorithms for Electric Power Dispatch Problem
Get PDFThe potential and effectiveness of the newly developed Pareto-based multiobjective evolutionary algorithms (MOEA) for solving a real-world power system multiobjective nonlinear optimization problem are comprehensively discussed and evaluated in this paper. Specifically, nondominated sorting genetic algorithm, niched Pareto genetic algorithm, and strength Pareto evolutionary algorithm (SPEA) have been developed and successfully applied to an environmental/economic electric power dispatch problem. A new procedure for quality measure is proposed in this paper in order to evaluate different techniques. A feasibility check procedure has been developed and superimposed on MOEA to restrict the search to the feasible region of the problem space. A hierarchical clustering algorithm is also imposed to provide the power system operator with a representative and manageable Pareto-optimal set. Moreover, an approach based on fuzzy set theory is developed to extract one of the Pareto-optimal solutions as the best compromise one. These multiobjective evolutionary algorithms have been individually examined and applied to the standard IEEE 30-bus six-generator test system. Several optimization runs have been carried out on different cases of problem complexity. The results of MOEA have been compared to those reported in the literature. The results confirm the potential and effectiveness of MOEA compared to the traditional multiobjective optimization techniques. In addition, the results demonstrate the superiority of the SPEA as a promising multiobjective evolutionary algorithm to solve different power system multiobjective optimization problems
Robust Damping Controls for a Unified Power Flow Controller
Get PDFThis article investigates the various damping controls of the unified power flow controller (UPFC). A detailed dynamic model of the UPFC including the possible damping control parameters has been derived. A method of determining the stable operating states of the nonlinear system model has been presented. Fixed parameter robust controllers for the identified controls have been designed satisfying the robustness conditions on performance and stability. The robust controller design has been carried out with the aid of a simple graphical 'loop-shaping' construction procedure. Simulation studies show that both robust series converter voltage magnitude and shunt converter phase angle provide extremely good damping. Combined application of the above two controls, however, gives the best damping profile over a wide range of operation. PI controllers having optimized gain settings were employed to evaluate the robustness of the proposed controllers