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Modeling and optimization of biodiesel from high free‐fatty‐acid chicken fat by non‐catalytic esterification and mussel‐shell‐catalyzed transesterification
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Abstract<sec><title>BACKGROUND

In this study, biodiesel was prepared from chicken fat via a transesterification reaction using Mussel shells as a catalyst. Pretreatment of chicken fat was carried out using non‐catalytic esterification to reduce the free fatty acid content from 36.28 to 0.96 mg KOH/g oil using an ethanol/ fat mole ratio equal to 115:1. In the transesterification reaction, the studied variables were methanol: oil mole ratio in the range of (6:1 ‐ 30:1), catalyst loading in the range of (9‐15) wt%, reaction temperature (55‐75 °C), and reaction time (1‐7) h. The heterogeneous alkaline catalyst was greenly synthesized from waste mussel shells throughout a calcination process at different calcination times of (1‐5) h and temperatures of (700‐900) °C. The catalyst was characterized using BET, SEM, EDX, XRD, and FTIR.

RESULTS

In the transesterification reaction, the best values of the studied parameters were: 21:1 methanol: oil molar ratio, 12 wt% catalyst loading, 5 h reaction time, and 63°C reaction temperature, which gave 96.2% methyl esters content. For catalyst synthesis, it was found that the optimum calcination conditions were 900 °C and 3 h, which resulted in a specific surface area of 10.5 m2/g and a large pore volume of 0.0033 cm3/g.

CONCLUSION

A calcium oxide catalyst was successfully prepared from mussel shells. This catalyst was used to transesterify the chicken fat into biodiesel. The prepared catalyst exhibited a high active surface area and a pore volume, confirming that the CaO catalyst produced from waste mussel shells worked effectively, steadily, and affordably to produce renewable biodiesel. The best working conditions for the transesterification reaction were determined using the central Composite Design method (CCD). © 2023 Society of Chemical Industry.

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This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional ANN has only the desired position and orientation of the end effector in the input pattern of neural network. In this paper, a six DOF Denso robotic arm with a gripper is controlled by ANN. The comprehensive experimental results proved the appl

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Brandt’s Hedgehog, <i>Paraechinus hypomelas</i> (Brandt, 1836), new to the mammal fauna of Iraq
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