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An experimental solar energy-assisted photocatalytic low-pressure dryer (SEPLD) for batch food dehydration was developed and tested in an attempt to completely utilize green energy and contribute to reducing greenhouse gas emissions. The dryer consists of high-transmittance glass used to absorb heat from solar radiation, by which means the water content of food products was vaporized and removed using a low-pressure generation system. The performance test, sensory evaluation, and compound analysis showed that the SEPLD had a faster dehydration rate than other drying processes, and samples retained better color and higher amounts of nutrients. Due to the photocatalyst's (TiO2) action, samples dried using the SEPLD had lower bacterial counts. During the food-drying process, the weight of materials is usually calculated with the drying time to obtain dehydration equations that can be used to further assess the efficiency of the drying equipment and drying performance, such as the drying characteristic curves, drying rate, diffusion coefficient, and so on. However, some information was not easy to obtain during the drying process. We spent a lot of time measuring the water content of a large number of raw materials, especially for those raw materials that were difficult to dry. In this study, some seasonal fruits (pineapple [Ananas comosus L. Merrill], tomato [Lycopersicon esculentum], and star fruit [Averhoa carambola L.]) were selected for the drying tests at 40, 50, and 60°C in the SEPLD. The grey prediction methodology (GPM) was used with water content data to establish the SEPLD drying model, and this was applied to simulate the drying process at different temperatures. The results showed that the accuracy of 60°C drying by GM(1,1) of the GPM was better than the other prediction models. It also indicated that the GPM was a feasible way to simulate the drying process. A patent for the SEPLD was granted and issued in Taiwan. [ABSTRACT FROM AUTHOR]

Abstract: Proper regulation of pH value is an important issue in the food industry for quality production. A food pH process usually has non-linear dynamics with system uncertainty. This study treats the pH regulation process of a reactor tank as a grey box with partially known system information. The behaviour of the process is predicted one-step ahead with a first-order grey model. A fuzzy controller takes the prediction together with the current system response to regulate the discharge of base (NaOH) or acid (HCl) solution into the reactor tank to arrive at a desired pH value. The integrated grey prediction fuzzy control (GPFC) strategy is simple in control-law derivation and system implementation and is efficient in computation. The developed GPFC was validated with perform base/acid titration and continuous acidification/deacidification control. The controlled system response error was trivial in the titration and was less than 1% in the continuous control under proper agitation of the reactants. The system was used to control Acetobacter xylinum fermentation for cellulose production. The GPFC scheme exerted smooth control action, achieved a trivial steady-state error in pH control, and yielded more cellulose and acetic acid products but consumed much less material than PID or manual control. [Copyright &y& Elsevier]