Over the last decade, food processing has become one of the greatest energy converting stages of the food production supply chain. The interdependency of food, water, and energy leads to a need for more water efficient and energy effective ways to produce food. These studies focus on poultry chilling, primarily comparing the potential options of media that could be used during the poultry chilling sub-process. The conventional poultry chilling approach typically involves the immersion of chicken within chilled water in order to quickly decrease the chicken temperature, thus hindering the growth of bacteria. This research is an initial investigation of ice slurry as an energy and water efficient, pathogen reducing, and financially feasible chiller medium in poultry processing. The financial feasibility and electrical energy demand of using ice slurry were explored in a techno-economic model in HOMER Energy, which is a micro-grid design and optimization software. The thermal cooling capacity of ice slurry and fluidity of the solution allows for generation and storage to occur during low electricity cost hours and an application during high electricity cost hours, thus creating savings in electricity costs associated with poultry chilling. During the poultry chilling experimentation, chickens were spiked with Salmonella as temperature probes measured their core body temperature throughout their immersion within the different media. Greater pathogen reductions, faster cooling times, and less water consumption compared to chilled water promotes ice slurry as an alternate medium in the poultry processing industry.

Made available in DSpace on 2017-07-10T18:08:07Z (GMT). No. of bitstreams: 1 Tulio Klassen.pdf: 1461464 bytes, checksum: 02d111496a486b94ea73232034f2aeb6 (MD5) Previous issue date: 2008-02-11 / The Artificial Neural Networks have been used with success for the description and modeling of processes in the most several areas of the knowledge, from economy, administration, artificial intelligence and even control of complex industrial processes. The process of chilling of chickens for immersion in cold water ("chillers") is complex and difficult to be modeled phenomenologicaly, because it involves transfer of heat, mass and transient regime, besides a great number of variables. In this work several architectures of artificial neural networks were used in the description and modeling of the process of chilling of the chickens, foreseeing the final temperature and the growth of weight of the carcasses. Also for comparison effect they were used an empiric model proposed by CARCIOFI & LAURINDO (2007) to describe the absorption of the water for the carcasses and the chilling model according to Newton's Law for the temperature of the carcasses. Different situations were tested changing the numbers of neurons of the entrance and hidden layers, and the number of layers. The data used were supplied by the SADIA - Toledo company for training and validation of the net. For the model twenty-five entrance variables were selected, as weight of the carcass, temperature before the chillers, temperature of the propilenoglicol shirt, flow of water in each module of the tanks, time of chilling and temperature of the renewal water, bubble intensity and amount of ice. The results obtained by the neural network and for Newton's Law they were not efficient to represent the final temperature of the carcass. The neural networks and the empiric model of CARCIOFI & LAURINDO (2007) went very efficient to esteem the amount of water absorbed for the carcasses. The obtained results showed that the net type with 4 x 12 x 4 neurons in the entrance layer, first and second hidden layers respectively was the best to represent the investigated system. / As Redes Neurais Artificiais têm sido empregadas com sucesso para a descrição e modelagem de processos nas mais diversas áreas do conhecimento, desde economia, administração, inteligência artificial e até controle de processos industriais complexos. O processo de resfriamento de frangos por imersão em água gelada ( chillers ) é complexo e difícil de ser modelado fenomenologicamente, pois envolve transferência de calor, massa e regime transiente, além de um grande número de variáveis. Neste trabalho foram empregadas diversas arquiteturas de redes neurais artificiais na descrição e modelagem do processo de resfriamento dos frangos, prevendo a temperatura final e o ganho de peso das carcaças. Também para efeito de comparação foram empregados um modelo empírico proposto por CARCIOFI & LAURINDO (2007) para descrever a absorção da água pelas carcaças e o modelo de resfriamento segundo a Lei de Newton para a temperatura das carcaças. Foram testadas diferentes situações alterando-se os números de neurônios das camadas de entrada e intermediária, e o número de camadas. Foram utilizados dados fornecidos pela empresa SADIA Toledo para treinamento e validação da rede. Para o modelo foram selecionadas vinte e cinco variáveis de entrada, como peso da carcaça, temperatura antes do resfriamento, temperatura da camisa de propilenoglicol, vazão de água em cada módulo dos tanques, tempo de resfriamento e temperatura da água de renovação, borbulhamento e quantidade de gelo. Os resultados obtidos pelas redes neurais e pela Lei de Newton não foram eficientes para representar a temperatura de saída da carcaça. As redes neurais e o modelo empírico de CARCIOFI & LAURINDO (2007) foram muito eficientes para estimar a quantidade de água absorvida pelas carcaças. Os resultados obtidos mostraram que a rede tipo 4 x 12 x 4 neurônios na camada de entrada, primeira intermediária e segunda intermediária respectivamente foi a que melhor representou o sistema investigado.