Optimization of cost effective culture medium for Sporosarcina pasteurii as biocementing agent using response surface methodology: Up cycling dairy waste and seawater. Journal of Cleaner Production, 253, https://doi.Biological precipitation of calcium carbonate using Sporosarcina pasteurii, has been developed in the recent decades for the civil applications such as improvement of soil engineering parameters, crack repair in concrete and remediation of contaminated soil. In order to facilitate the application of this method, cost optimization and production simplification are the main challenges. In this study, optimization of culture media in order to reducing the cost of production of urease bacteria and achieving an ecofriendly process were investigated. Therefore, inexpensive nutrients and water resources were used in composition of the culture media and sanitized media were examined instead of sterilized ones. A central composite design was used to design required tests to investigate yeast extract, whey, heating temperature and the Caspian Sea water effects. The response surface method predicated that the maximum specific urease activity (16.50 mm urea.min-1.OD-1) could be achieved at 9.94 g.l-1 yeast extract, 23.43 g.l-1 whey, heating temperature of 128.6 °C and 0% seawater. When the medium contained 12.31 g.l-1 yeast extract, 23.43 g.l-1 whey and 0% seawater was sanitized by heating at 100.0 °C, 64% of optimum specific urease activity was reserved; and it could improve the unconfined compressive strength of a poorly graded sand up to 520 kPa. In case of using the Caspian Sea water as the solvent of culture media, the specific urease activity changes could be ignored. Application of whey as a nutrient source has shown promising results that could relieve some environmental and economic concerns. This approach is promising to achieve an ecofriendly biocementation technology for large scale applications.
Over the decades, a number of empirical correlations have been proposed to relate the Compression Index of normally consolidated soils to other soil parameters, such as the natural water content, liquid limit, plasticity index and void ratio. In this article too it has been attempted to establish a correlation between compression index and physical properties for the clayey soils of Mazandaran region. Due to the multiple effects of various parameters, Artificial Neural Network (ANN) has been adapted for predicting the compression index from more simply determined index properties. In order to develop the ANN model, four hundred consolidation tests for soils sampled at 125 construction sites in the province of Mazandaran, in the north of Iran were collected and 90% of these were used to train the prediction model and the other 10% were used to test it. A comparison was carried out between the experimentally measured compression indexes with the predictions. Furthermore, the predictions of a number of previously proposed empirical correlations were obtained using the available data and it has been shown that an improvement of 1-4% with respect to the other correlations has been achieved.
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