Along with the development of motor vehicle industry technology at this time, the fuel demand is also increasing while the supply is running low. Thus, alternative fuels are needed to meet these energy needs. This study aims to explain the physical and chemical characteristics of a fuel mixture (MF) between palm sap bioethanol with premium fuel. The results showed that the higher the bioethanol concentration of the palm sap, the higher the MF's viscosity, but the lower the heat of the fuel. This decrease is caused by differences in the heating value of the two fuels. The MF's high heat burn value is blue, while the low heat value of the flame is reddish yellow. The results of this study are very important as a basis for the development of bioethanol from palm sap as an environmentally friendly vehicle-fuel substitute material.

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A continuous circulating of sorbent powder for sorption and desorption process in two connected fluidized beds has been developed. Two fluidized beds are arranged next to each other and connected by spiral tubes. The experiments were carried out under the various conditions such as air velocity, desorption air temperature, and sorbent powder circulation rate. Sorption and desorption characteristics of sorbent powder of the organic sorption materials show that sorption and desorption performance promoted by increasing air velocity and desorption air temperature. It was found that the spiral revolution speed has optimal value on the humidification. Furthermore, the non-dimensional correlation equations were obtained for water vapor mass transfer under sorption and desorption process in terms of relevant non-dimensional parameters.

Currently, some coffee production centers still perform classification manually, which requires a very long time, a lot of labor, and expensive operational costs. Therefore, the purpose of this research was to design and test the performance of a coffee bean classifier that can accelerate the process of classifying beans. The classifier used consisted of three main parts, namely the frame, the driving force, and sieves. The research parameters included classifier work capacity, power, specific energy, classification distribution and effectiveness, and efficiency. The results showed that the best operating conditions of the coffee bean classifier was a rotational speed of 91.07 rpm and a 16° sieve angle with a classifier working capacity of 38.27 kg/h: the distribution of the seeds retained in the first sieve was 56.77%, the second sieve was 28.12%, and the third sieve was 15.11%. The efficiency of using a classifier was found at a rotating speed of 91.07 rpm and a sieve angle of 16°. This classifier was simple in design, easy to operate, and can sort coffee beans into three classifications, namely small, medium, and large.