Probabilistic reservoir operation using Bayesian stochastic model and support vector machine

Available water resources are often not sufficient or too polluted to satisfy the needs of all water users. Therefore, allocating water to meet water demands with better quality is a major challenge in reservoir operation. In this paper, a methodology to develop operating strategies for water release from a reservoir with acceptable quality and quantity is presented. The proposed model includes a genetic algorithm (GA)-based optimization model linked with a reservoir water quality simulation model. The objective function of the optimization model is based on the Nash bargaining theory to maximize the reliability of supplying the downstream demands with acceptable quality, maintaining a high reservoir storage level, and preventing quality degradation of the reservoir. In order to reduce the run time of the GA-based optimization model, the main optimization model is divided into a stochastic and a deterministic optimization model for reservoir operation considering water quality issues.The operating policies resulted from the reservoir operation model with the water quantity objective are used to determine the released water ranges (permissible lower and upper bounds of release policies) during the planning horizon. Then, certain values of release and the optimal releases from each reservoir outlet are determined utilizing the optimization model with water quality objectives. The support vector machine (SVM) model is used to generate the operating rules for the selective withdrawal from the reservoir for real-time operation. The results show that the SVM model can be effectively used in determining water release from the reservoir. Finally, the copula function was used to estimate the joint probability of supplying the water demand with desirable quality as an evaluation index of the system reliability. The proposed method was applied to the Satarkhan reservoir in the north-western part of Iran. The results of the proposed models are compared with the alternative models. The results show that the proposed models could be used as effective tools in reservoir operation.     

Probabilistic correlation between laboratory and field liquefaction potentials using relative state parameter index (ξR)

Cyclic triaxial tests on reconstituted sand samples are broadly employed in practice while they ignore the inherent characteristics of soil in field condition such as aging, fabric, and prior strain history. Relative state parameter index, ξ_R, is utilized in a probabilistic framework to adjust the cyclic triaxial resistance ratio of sands at 15 uniform cycles (CRR_tx,15) to field condition. A wide-ranging database containing the results of cyclic triaxial tests conducted on reconstituted samples has been compiled to derive a correlation between relative state parameter index (ξ_R) and triaxial cyclic resistance ratio. The adjustment coefficients proposed by researchers are employed to correct CRR_tx,15−ξ_R relationship for actual field condition. The adjusted CRR_tx,15−ξ_R relationships are applied to a database of field liquefaction case histories composed of both SPT and CPT based data and their performances in field condition are evaluated. It is demonstrated that constant triaxial-to-field adjusting coefficients cannot ever predict conservative results. Logistic regression method is employed to derive a field probabilistic criterion that obtains the likelihood of liquefaction initiation in terms of ξ_R. The ξ_R-based boundary curve standing for 20% likelihood of liquefaction initiation is found to be the most conservative limit state boundary to be used in field conditions. Finally, the triaxial and field CRR−ξ_R relationships are composed and a probabilistic triaxial-to-field adjustment coefficient is proposed in terms of ξ_R and a given liquefaction probability. It is anticipated that the proposed relationship could reasonably correct the results of cyclic triaxial testing on freshly reconstituted sand samples.     

Further Study on the Kinetics of Bioremediation of Soils Contaminated with the Herbicide 2,4‐Dichlorophenoxy Acetic Acid

The bioremediation of soil contaminated with the herbicide 2,4‐dichlorophenoxy acetic acid has been studied applying the bacterium Ralstonia eutropha. The effects of temperature, pH, and moisture content of soils on the rate of degradation of this herbicide have been investigated. The Taguchi experimental design method was applied to determine the relative impacts of the pertinent system variables. According to this approach, four series of experiments were performed under various operating conditions. In all four series the herbicide was degraded efficiently, however, the highest rate was observed at a temperature of 35°C, pH 6, and at 80% moisture content of the soil. A kinetic model for the biodegradation of the herbicide was developed for this particular conditions based on the Monod type growth equation and autocatalytic behavior of the decomposition reaction. A close correlation has been observed between the experimental data and those predicted from the model. The degree of agreement was between 85–95%.     

Discrimination of Different Erosion Levels of Porphyry Cu Deposits using ASTER Image Processing in Eastern Iran: a Case Study in the Maherabad, Shadan, and Chah Shaljami Areas

The Lut block, eastern Iran, is one of the most extensive Cenozoic magmatic rocks, that show suitable targets for porphyry Cu-Au and high-sulfidation epithermal Au related to porphyry Cu-Au mineralization. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer （ASTER） was used to identify different erosion levels of three porphyry Cu deposits, including Maherabad, Shadan, and Chah Shaljami, located in the Lut block volcanic-plutonic belt. Alteration minerals, including kaolinite, dickite, alunite, jarosite, epidote, chlorite, montmorillonite, muscovite, biotite, orthoclase, gypsum, and quartz, are selected to map different alteration zones and erosion levels. Distributions of end-members are mapped by using the SAM and MTMF in VNIR and SWIR of ASTER bands and the results are evaluated against the field studies. For some end-members, the results of SAM processing are more reliable compared to the MTMF because the latter looses field spectra. The use of angle threshold in the SAM, and MF-score and infeasibility value in the MTMF or low abundance of some end-members, and finally comparison of output images of spectral processing show good correlation with alteration maps. Differentiation and explanation of various erosion levels of porphyry Cu deposits are done successfully by using the ASTER sensor data.