Ultrasonic Measurements and Machine Learning for Monitoring the Removal of Surface Fouling during Clean-in-Place Processes

Cleaning is an essential operation in the food and drink manufacturing sector, although it comes with significant economic and environmental costs. Cleaning is generally performed using autonomous Clean-in-Place (CIP) processes, which often over-clean, as suitable technologies do not exist to determine when fouling has been removed from the internal surfaces of processing equipment. This research combines ultrasonic measurements and machine learning methods to determine when fouling has been removed from a test section of pipework for a range of different food materials. The results show that the proposed methodology is successful in predicting when fouling is present on the test section with accuracies up to 99% for the range of different machine learning algorithms studied. Various aspects relating to the training data set and input data selection were studied to determine their effect on the performance of the different machine learning methods studied. It was found that the classification models performed better when data points were extracted directly from the ultrasonic waves and when data sets were combined for different fouling materials

Modelling of small capacity absorption chillers driven by solar thermal energy or waste heat

Aquesta recerca es centra en el desenvolupament de models en règim estacionari de màquines d’absorció de petita potència, els quals estan basats en dades altament fiables obtingudes en un banc d’assajos d’última tecnologia. Aquests models podran ser utilitzats en aplicacions de simulació, o bé per a desenvolupar estratègies de control de supervisió dels sistemes d’aire condicionat amb màquines d’absorció. Per tant, l’objectiu principal d’aquesta investigació és desenvolupar i descriure una metodologia comprensible i que englobi el procés sencer: tant els assajos, com la modelització, com també el desenvolupament d’una estratègia de control per a les màquines d’absorció de petita potència. Basant-se en la informació obtinguda de forma experimental en el banc d’assajos, s’han desenvolupat cinc models, cadascun amb una base teòrica diferent. Els resultats mostren que és possible obtenir models empírics summament precisos utilitzant únicament com a paràmetres d’entrada les variables dels circuits externs d’aigua. Aquest treball finalitza amb la proposta de dues estratègies òptimes de control i el seu ús per al control on-line de sistemes basats en refredadores tèrmiques d’absorció.This research deals with the development of the simple, yet accurate steady-state models of small capacity absorption machines which are based on highly reliable data obtained in the state-of-the-art test bench. These models can further be used in simulation tools or to develop supervisory control strategies for air-conditioning systems with absorption machines. Therefore, the main aim of this research is to develop and to describe a comprehensive methodology which encloses entire process which consists of testing, modelling and control strategy development of small capacity absorption machines. Five different models are developed based on the experimental data obtained in the test bench. The results show that it is possible to develop highly accurate empirical models by using only the variables of external water circuits as input parameters. Finally, two optimal control strategies are developed to demonstrate how these models can be used for on-line control of absorption systems

Modified Stacked Autoencoder Using Adaptive Morlet Wavelet for Intelligent Fault Diagnosis of Rotating Machinery

Intelligent fault diagnosis techniques play an important role in improving the abilities of automated monitoring, inference, and decision making for the repair and maintenance of machinery and processes. In this article, a modified stacked autoencoder (MSAE) that uses adaptive Morlet wavelet is proposed to automatically diagnose various fault types and severities of rotating machinery. First, the Morlet wavelet activation function is utilized to construct an MSAE to establish an accurate nonlinear mapping between the raw nonstationary vibration data and different fault states. Then, the nonnegative constraint is applied to enhance the cost function to improve sparsity performance and reconstruction quality. Finally, the fruit fly optimization algorithm is used to determine the adjustable parameters of the Morlet wavelet to flexibly match the characteristics of the analyzed data. The proposed method is used to analyze the raw vibration data collected from a sun gear unit and a roller bearing unit. Experimental results show that the proposed method is superior to other state-of-the-art methods

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

NASA SBIR abstracts of 1992, phase 1 projects

The objectives of 346 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1992 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 346, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1992 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

30th International Conference on Condition Monitoring and Diagnostic Engineering Management (COMADEM 2017)

Proceedings of COMADEM 201

Smart grid optimized operation driven by reinforcement learning

This thesis focuses on the development of a reinforcement learning model for the operation and demand response control of a smart grid. First, a generic problem is formulated to define the demand response control. Then a study case is proposed with different locations of distributed energy resources and flexible components for reducing the cost associated with its grid con- sumption and safety management. The potential application of different deep reinforcement learning models with different activation functions and network shapes, among them, will be compared and analysed for the grid operation. The goal is to find a deep reinforcement learning model to optimize the demand side of energy management of a smart grid, that achieves better results than other existing approaches. Finally, a new policy for deep reinforcement learning algorithms will be proposed. This will provide a tool to guide the energy management of elec- trical distribution grids with high penetration of renewable energy sources

ReSCon '12, Research Student Conference: Book of Abstracts

The fifth SED Research Student Conference (ReSCon2012) was hosted over three days, 18-20 June 2012, in the Hamilton Centre at Brunel University. The conference consisted of 130 oral and 70 poster presentations, based on the high quality and diverse research being conducted within the School of Engineering and Design by postgraduate research students. The conference is held annually, and ReSCon plays a key role in contributing to research and innovations within the School