Gravel dust and limestone dust are types of solid waste materials that are obtained from the crusher plant. These materials are dumped-off in high amount causing a negative impact on the environment and creating serious hazards on health. As the construction activities are increased in developing countries, the demand of crushed gravel and crushed limestone for roads, airfields, railway ballast, buildings and concrete work are increased. This study attempted to investigate the effect of gravel dust and limestone dust on geotechnical properties of clayey soil. Testing program including Atterberg limits, compaction, CBR and free swell tests, were performed on clay with the dust at varying amounts (10, 20, 30 and 40%). The results showed that the Atterberg limits of clay decrease in proportion to increases in the amount of dust. The increase in gravel dust contents decreased the compaction characteristics of clay. However, the MDD increased and OMC decreased with increase in limestone dust contents. A series of soaked CBR tests were conducted on the clay-dust mixtures of gravel dust and limestone dust. The dust was mixed with the clay of different weight percentages. The results showed a general increase in the CBR value of clay with the addition of dust. The CBR value increased gradually with the gravel dust content. The maximum CBR was obtained at optimum limestone dust content, approximately 20%. Swelling percentages decreased gradually with the dust content. The tests results revealed that it is possible to use gravel dust and limestone dust for improving the properties of clayey soil.

No abstract

No abstract

Biodiesel is potential renewable and clean energy, which can be produced form wide range of waste materials. This study employs a hybrid response surface methodology (RSM) and crow search algorithm (CSA) as novel tool for global optimization of transesterification reaction parameters to maximize biodiesel synthesis from papaya seed‐derived waste oil. Catalyst (NaOH) dose, methanol to oil molar ratio (M:O), and reaction time were considered independent factors, while biodiesel yield was taken as a dependent variable. The experimentally produced biodiesel was characterized by gas chromatography–mass spectrometry analysis. The experiments were developed based on RSM with Box–Behnken design matrix, which was subsequently used for modeling, optimization and model validation. Initially, a quadratic regression model was developed following RSM technique, correlating the transesterification reaction parameters and biodiesel yield. Afterward, the CSA coupled with RSM approach was employed to assess the global optimization. A highest biodiesel yield of 99.48% was attained with a catalyst (NaOH) dose of 0.5 wt%, M:O of 8.5:1 at a reaction time of 40 min. The results acquired by RSM‐CSA were also compared with the results achieved by desirability function‐based optimization technique. Further, the optimal set for maximizing biodiesel yield was validated experimentally with an error margin of 2.0%. These observations indicate that the hybrid RSM‐CSA is an efficient and economic approach to optimize the process conditions for biodiesel production from alternative sources.

No abstract