The synthesis of fatty acid methyl ester (FAME) from the high- and low-acid-content feedstock of crude palm oil (CPO) and karanj oil (KO) was conducted over CaO-La2O3-Al2O3 mixed-oxide catalyst. Various reaction parameters were investigated using a batch reactor to identify the best reaction condition that results in the highest FAME yield for each type of oil. The transesterification of CPO resulted in a 97.81% FAME yield with the process conditions of 170°C reaction temperature, 15:1 DMC-to-CPO molar ratio, 180min reaction time, and 10wt.% catalyst loading. The transesterification of KO resulted in a 96.77% FAME yield with the conditions of 150°C reaction temperature, 9:1 DMC-to-KO molar ratio, 180min reaction time, and 5wt.% catalyst loading. The properties of both products met the ASTM D6751 and EN 14214 standard requirements. The above results showed that the CaO-La2O3-Al2O3 mixed-oxide catalyst was suitable for high- and low-acid-content vegetable oil.
Fouling formation in reactor vessels poses a serious threat to the safe operation of the industrial low-density polyethylene (LDPE) polymerization. Fouling also degrades the polymer quality and causes productivity loss to some extent. In this work, neural Wiener model predictive control (NWMPC) is introduced to address the fouling concern. In addition, a soft sensor model is used to activate the fouling-defouling (F-D) mechanism when the fouling surpasses the thickness limit to prevent vessel overheating. NWMPC is proven to be fast, stable, and robust under various control scenarios. The use of a soft sensor model in conjunction with NWMPC enables the online monitoring and controlling of the F-D processes. When comparison is made with a state space (SSMPC) utilizing only the linear block, NWMPC is found to be able to control the LDPE grade with quicker grade transition and lower resource consumption.