Alum as a heterogeneous catalyst for the transesterification of palm oil

Alum has been taken beyond its traditional roles as a water treatment chemical and a confectionary additive to a new role as a catalytic precursor in biodiesel production. Its catalytic potentials were empirically proved via palm oil transesterification with methanol and application of solid state instrumental characterization techniques. The catalyst was very clean, efficient, simple and cheap to produce, and could be clearly separated from the reaction products. When the reaction was carried out under the conditions of catalyst to oil ratio of 7.09 wt%, reaction time of 12 h and temperature of 170 °C, methanol to oil molar ratio of 18:1 and catalyst preconditioned at 550 °C, the yield of fatty acid methyl ester (FAME) obtained was 92.5 wt%.     

Kinetics and mechanism of maleic acid hydroxylation by hydrogen peroxide. Part I: Formal kinetic studies by immobilized catalysis in a pseudo-homogeneous system

Formal kinetic studies of the epoxidation and hydroxylation of maleic acid by hydrogen peroxide (H₂O₂) have been carried out in the presence of a molybdenum salt, as catalyst, immobilized on Amberlite IRA-400 polymer resin in the chloride form. Immobilization of the catalyst on the resin was by a sorption process. Hydroxylation by immobilized catalysis afforded higher H₂O₂ selectivity and increased product yield in excess of 85%. Analysis of kinetic results shows that the reaction in the pseudo-homogeneous system follows the typical pseudo-zero order dependence on H₂O₂ concentration and is first order with respect to maleic acid concentration. However, the rate model developed for the immobilized catalyst fits the experimental data smoothly and parameter estimation using the Lineweaver–Burk plot fulfils the kinetic consistency tests without additional mathematical manipulation. The problem of activity and stability of the present catalyst was also investigated.     

Modeling of particle size distribution and energy consumption of wet milled maize at varying soaking period and method in the production of Ogi

Energy consumptions and particles size distribution of soaked maize grains at varying time were studied and modeled. Rosin–Rammler–Bennet (RRB) model well fitted first milling size distribution with a high coefficient of determination (R²) and low root mean square error (RSME). The milling energy of maize grains decreased significantly (p?<?0.05) with increase in soaking time. The milling energy decrease from 32 to 8.72?kWh/kg and 32.00 to 9.00?kWh/kg for maize variety E9W at 24th hour soaking conditions of 28 and 65°C, respectively. Similar observations for A4W, B2Y and C3Y at 24?h of soaking were recorded. The Work index, Kick’s and Rittinger’s constants decreased with increase in soaking time. There was significant difference (p?<?0.05) in values obtained for Bond work index, Rittinger’s and Kick’s constants; these decreased with increase in soaking time. Predicted energy consumption followed similar trend. The interaction effect between energy consumption, Moisture content, and Milling time showed a high R² (0.8767–0.99349); while the regressed model for determining energy consumption from relationship between the mass, moisture content, milling time and the ratio of the geometric diameter mean and final size of the product were also established in this work with R² ranging from 0.9355 to 0.967.     

An improved particle swarm optimiser based on swarm success rate for global optimisation problems

Inertia weight is one of the control parameters that influences the performance of particle swarm optimisation (PSO) in the course of solving global optimisation problems, by striking a balance between exploration and exploitation. Among many inertia weight strategies that have been proposed in literature are chaotic descending inertia weight (CDIW) and chaotic random inertia weight (CRIW). These two strategies have been claimed to perform better than linear descending inertia weight (LDIW) and random inertia weight (RIW). Despite these successes, a closer look at their results reveals that the common problem of premature convergence associated with PSO algorithm still lingers. Motivated by the better performances of CDIW and CRIW, this paper proposed two new inertia weight strategies namely: swarm success rate descending inertia weight (SSRDIW) and swarm success rate random inertia weight (SSRRIW). These two strategies use swarm success rates as a feedback parameter. Efforts were made using the proposed inertia weight strategies with PSO to further improve the effectiveness of the algorithm in terms of convergence speed, global search ability and improved solution accuracy. The proposed PSO variants, SSRDIW_PSO and SSRRIW_PSO were validated using several benchmark unconstrained global optimisation test problems and their performances compared with LDIW-PSO, CDIW-PSO, RIW-PSO, CRIW-PSO and some other existing PSO variants. Empirical results showed that the proposed variants are more efficient.