Estimating the Parental Magma Composition and Temperature of the Xiaohaizi Cumulate-Bearing Ultramafic Rock: Implication for Magma Evolution of the Tarim Large Igneous Province, Northwestern China

The ultramafic dikes in the Tarim large igneous province（Tarim LIP）, exposed in the Xiaohaizi area in the northwestern Tarim Basin of northwestern China, have porphyritic textures, and the olivine and clinopyroxene are as the major phenocryst phases. The groundmass therein consists of clinopyroxene, plagioclase and Fe-Ti oxides, with the cryptocrystalline texture. The olivine phenocrysts in one typical ultramafic dike have Fo（Mg/（Mg＋Fe）） numbers ranging from 73 to 85, which are not in equilibrium with the olivine（Mg# of 89） from the host rock crystalized. Combined with microscope observation, both the olivine and clinopyroxene phenocrysts as well as some Fe-Ti oxides in the ultramafic rock are accounted as cumulates. The liquid（parental magma） composition of SiO2 of 45.00 wt.%–48.82 wt.%, MgO of 9.93 wt.%– 18.56 wt.%, FeO of 5.85 wt.%–14.17 wt.%, CaO of 7.54 wt.%–11.52 wt.%, Al2O3 of 8.70 wt.%–11.62 wt.% and TiO2 of 0.00 wt.%–3.43 wt.% in the Xiaohaizi ultramafic rock was estimated by mass balance, and the results show a reasonable liquid proportion in the cumulate-bearing ultramafic dike（ca. 45%–60% in the whole rock）. The estimated parental magma composition corresponds to a melting temperature of 1 300–1 550 oC, which is equal or higher than those of a normal asthenosphere mantle, supporting the involvement of a mantle plume. Combined with other previous studies, an evolution model for the formation processes of the Xiaohaizi ultramafic dike of the Tarim LIP is proposed.

MPC-based interval number optimization for electric water heater scheduling in uncertain environments

In this paper,interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling.First of all,interval numbers are used to describe uncertain parameters including hot water demand,ambient temperature,and real-time price of electricity.Moreover,the traditional thermal dynamic model of electric water heater is transformed into an interval number model,based on which,the day-ahead load scheduling problem with uncertain parameters is formulated,and solved by interval number optimization.Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices.Furthermore,the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day.Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand,ambient temperature,and real-time price of electricity,enabling customers to flexibly adjust electric water heater control strategy.