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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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Thu May 01 2008
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2008 International Conference On Computer And Communication Engineering
A binary Particle Swarm Optimization for attacking knapsacks Cipher Algorithm
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The biometric-based keys generation represents the utilization of the extracted features from the human anatomical (physiological) traits like a fingerprint, retina, etc. or behavioral traits like a signature. The retina biometric has inherent robustness, therefore, it is capable of generating random keys with a higher security level compared to the other biometric traits. In this paper, an effective system to generate secure, robust and unique random keys based on retina features has been proposed for cryptographic applications. The retina features are extracted by using the algorithm of glowworm swarm optimization (GSO) that provides promising results through the experiments using the standard retina databases. Additionally, in order t

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Atan regularized for the high dimensional Poisson regression model
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Variable selection in Poisson regression with high dimensional data has been widely used in recent years. we proposed in this paper using a penalty function that depends on a function named a penalty. An Atan estimator was compared with Lasso and adaptive lasso. A simulation and application show that an Atan estimator has the advantage in the estimation of coefficient and variables selection.

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Publication Date
Sat Jun 01 2024
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International Journal Of Advanced And Applied Sciences
High-accuracy models for iris recognition with merging features
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Due to advancements in computer science and technology, impersonation has become more common. Today, biometrics technology is widely used in various aspects of people's lives. Iris recognition, known for its high accuracy and speed, is a significant and challenging field of study. As a result, iris recognition technology and biometric systems are utilized for security in numerous applications, including human-computer interaction and surveillance systems. It is crucial to develop advanced models to combat impersonation crimes. This study proposes sophisticated artificial intelligence models with high accuracy and speed to eliminate these crimes. The models use linear discriminant analysis (LDA) for feature extraction and mutual info

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Developing the high performance practices to attain Organizational effectiveness
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Abstract

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High-Resolution Location Estimation Utilizing Single-Cell Massive MIMO
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Application of Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) Method
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The paper aims is to solve the problem of choosing the appropriate project from several service projects for the Iraqi Martyrs Foundation or arrange them according to the preference within the targeted criteria. this is done by using Multi-Criteria Decision Method (MCDM), which is the method of Multi-Objective Optimization by Ratios Analysis (MOORA) to measure the composite score of performance that each alternative gets and the maximum benefit accruing to the beneficiary and according to the criteria and weights that are calculated by the Analytic Hierarchy Process (AHP). The most important findings of the research and relying on expert opinion are to choose the second project as the best alternative and make an arrangement acco

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