Estimation of soil specific surface area using some mechanical properties of soil by artificial neural networks

Soil specific surface area (SSA) is an important property of soil. Depending on the measurement techniques, determination of the SSA is costly and time consuming. Hence, a limited number of studies have been conducted to predict the SSA from the soil variables. In this study, the soil samples were taken from the literature. Fractal parameters (FP) were calculated by the model of Bird et al. (European Journal of Soil Science 51, 55–63, 2000) used as the input variables to predict the SSA. Some studies have been carried out on the prediction capability of the different parameters using the artificial neural networks (ANNs). The ANNs were further used and 20 models were developed to investigate the value of input variables to predict the SSA. The results showed that the PTF13 (RMSE?=?0.13) and PTF18 (RMSE?=?0.13) with the input variables of particle-size distribution and Atterberg limits revealed better performance than the other PTFs (in the training step). It is because of the fact that free swelling index (FSI) and Atterberg limits were closely correlated to the soil clay mineralogy as one of the important factors controlling the SSA. In general, this results demonstrated that the PTF9 with the variables of sand, clay, plastic limit (PL), liquid limit (LL), and FSI showed the best (RMSE?=?0.37) results in the estimation of the SSA. In conclusion, there was not a strong correlation between the soil mechanical properties and SSA but also ANNs were a suitable method to predict the SSA from the soil variables.     

Microbial oxygen uptake in sludge as influenced by compost physical parameters

The wide range of optimal values reported for the physical parameters of compost mixtures suggest that their interactive relationships should be investigated. The objective of this study was to examine the microbial O₂ uptake rate (OUR) in 16 sludge waste recipes, offering a range of moisture content (MC), waste/bulking agent (W/BA) ratio and BA particle size levels determined using a central composite experimental design. The 3 kg samples were maintained at a constant temperature and aeration rate for 28 days, during which a respirometer recorded O₂ uptake to provide a measure of microbial activity and biodegradability. The cumulative O₂ consumption after 14 and 28 days was found to be significantly influenced by MC, W/BA ratio, BA particle size and the interaction between MC and W/BA ratio (p < 0.05). Using multivariate regression analysis, the experimental data was used to generate a model with good predictive ability for cumulative O₂ consumption after 28 days as a function of the significant physical variables (R² = 0.84). The prediction of O₂ uptake by the model depended highly on the interaction between MC and W/BA ratio. A MC outside of the traditional 50–60% (wet basis) range still resulted in a high level of microbial O₂ uptake as long as the W/BA ratio was adjusted to maintain a suitable O₂ exchange in the sample. The evolution of OUR in the samples was also investigated, uncovering strong associations between short and long-term respirometric indices, such as peak OUR and cumulative O₂ consumption (p < 0.005). Combining peak OUR data with cumulative O₂ consumption after 14 days allowed for accurate predictions of cumulative O₂ after 28 days of aeration (R² = 0.96), implying that future studies need only run trials up to 14 days to evaluate the overall O₂ consumption or biodegradability of similar sludge mixtures.     

Compost mixture influence of interactive physical parameters on microbial kinetics and substrate fractionation

Composting is a feasible biological treatment for the recycling of wastewater sludge as a soil amendment. The process can be optimized by selecting an initial compost recipe with physical properties that enhance microbial activity. The present study measured the microbial O₂ uptake rate (OUR) in 16 sludge and wood residue mixtures to estimate the kinetics parameters of maximum growth rate μ_m and rate of organic matter hydrolysis K_h, as well as the initial biodegradable organic matter fractions present. The starting mixtures consisted of a wide range of moisture content (MC), waste to bulking agent (BA) ratio (W/BA ratio) and BA particle size, which were placed in a laboratory respirometry apparatus to measure their OUR over 4 weeks. A microbial model based on the activated sludge process was used to calculate the kinetic parameters and was found to adequately reproduced OUR curves over time, except for the lag phase and peak OUR, which was not represented and generally over-estimated, respectively. The maximum growth rate μ_m, was found to have a quadratic relationship with MC and a negative association with BA particle size. As a result, increasing MC up to 50% and using a smaller BA particle size of 8–12 mm was seen to maximize μ_m. The rate of hydrolysis K_h was found to have a linear association with both MC and BA particle size. The model also estimated the initial readily biodegradable organic matter fraction, MB₀, and the slower biodegradable matter requiring hydrolysis, MH₀. The sum of MB₀ and MH₀ was associated with MC, W/BA ratio and the interaction between these two parameters, suggesting that O₂ availability was a key factor in determining the value of these two fractions. The study reinforced the idea that optimization of the physical characteristics of a compost mixture requires a holistic approach.     

Impact of incorporating customer preference in sustainable remanufacturing of commercial returns

This paper investigates optimum production parameters for a reverse supply chain for manufacturing of primary products and remanufacturing of commercial returns (products returned by customers for refund or exchange). The market for the product consists of two categories, the primary and remanufactured products. The demands for these markets are independent and considered to be random variables following a normal distribution function. The approach presented in this work differs from many previously published works because the acceptability of products varies among customers. The interaction between the designed quality and variable customers’ preferences determines the likelihood of a product being returned. Two major decision variables targeted in this study are the production cycle time and the targeted quality for production of parts used in the product. Through an analytical formulation and numerical examples, a relationship between the total profit of the system and the two decision variables is developed and optimised. The analysis demonstrates that the total profit of the hybrid system could be increased significantly by targeting the optimum targeted (not necessarily the highest) values for quality of parts and the optimum cycle length. And this objective could be accomplished with significant gain with respect to sustainability and waste reduction.