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Nowadays, environmental energy resources, especially mechanical vibrations, have attracted the attention of researchers to provide energy for low-power electronic circuits. A common method for environmental mechanical energy harvesting involves using piezoelectric materials. In this study, a spiral multimode piezoelectric energy harvester was designed and fabricated. To achieve wide bandwidth in low frequencies (below 15 Hz), the first three resonance frequencies of the beam were designed to be close to each other. To do this, the five lengths of the substrate layer were optimized by the Taguchi method, using an L27 orthogonal array. Each experiment of the Taguchi method was then simulated in ANSYS software. Next, the optimum level of each design variable was obtained. A test rig was then constructed based on the optimum design values and some experimental investigations were conducted. A good correlation was observed between measured and the finite element results. [ABSTRACT FROM AUTHOR]

In this paper, an analytical approach was used to formulate for gathering energy from ambient sources of vibrations. The apparatus consists of a cantilevered beam harvester with a piezoelectric patch. A coupled electromechanical modal model based on Euler–Bernoulli theory is used. The governing equations were extracted using Hamilton’s principle and then were discretized by using the Rayleigh–Ritz approach. The expected value of the electrical power output was obtained for a weakly stationary, Gaussian bandpass with zero mean base excitation. Next, a numerical solution for the beam under band-limited ambient random acceleration as the input was calculated and validated by experiment. The numerical results closely correlated to the experimental data with a deviation of about 10%. [ABSTRACT FROM AUTHOR]

Cocoa butter analog was prepared from camel hump fat and tristearin by enzymatic interesterification in supercritical carbon dioxide (SC-CO2) using immobilized Thermomyces lanuginosus lipase (Lipozyme TL IM) as a biocatalyst. Optimal process conditions were determined using neural networks and genetic algorithm optimization. Response surfaces methodology was used to design the experiments to collect data for the neural network modelling. A general regression neural network model was developed to predict the response of triacylglycerol (TAG) distribution of cocoa butter analog from the process pressure, temperature, tristearin/camel hump fat ratio, water content, and incubation time. A genetic algorithm was used to search for a combination of the process variables for production of most similar cocoa butter analog to the corresponding cocoa butter. The combinations of the process variables during genetic algorithm optimization were evaluated using the neural network model. The pressure of 10 MPa; temperature of 40 °C; SSS/CHF ratio of 0.6:1; water content of 13 % (w/w); and incubation time of 4.5 h were found to be the optimum conditions to achieve the most similar cocoa butter analog to the corresponding cocoa butter.