Carbothermal hydrogen reduction (CHR) of ammonium heptamolybdate impregnated activated charcoal (AC) yields a mixed Mo 2 C/MoO x C y catalyst. As the CHR temperature increases (from 600 to 800 °C) the Mo 2 C content increases. At 675 °C graphite networks are generated that attach to the β-Mo 2 C particles, and at ≥700 °C agglomeration and sintering occur, all of which decrease catalyst activity. An optimal CHR temperature of ∼650 °C is identified based on the catalyst activity for the hydrodeoxygenation (HDO) of 4-methylphenol (4-MP) at 350 °C and 4.3 MPa H 2 and the high selectivity for direct deoxygenation (DDO: to yield toluene) versus hydrogenation (HYD: to yield cyclohexane). The Mo 2 C/MoO x C y catalysts have higher DDO selectivity than MoP, MoO 2 , or MoS 2 when operated at similar conditions. The apparent activation energies for DDO (125 kJ/mol) and HYD (89 kJ/mol) are invariant among the catalysts with varying Mo 2 C content, but the rate per g Mo correlates with the CO uptake. The fact that the kinetics are not strong functions of the CHR reduction temperature and hence relative content of Mo 2 C versus MoO x C y suggests that the active site of the catalyst is a result of O adsorption and/or exchange with the catalyst during reaction, and these active sites occur on both Mo 2 C and MoO x C y during the HDO reaction.
In this paper, a novel data-driven model-free adaptive predictive control method based on lazy learning technique is proposed for a class of discrete-time single-input and single-output nonlinear systems. The feature of the proposed approach is that the controller is designed only using the input-output (I/O) measurement data of the system by means of a novel dynamic linearization technique with a new concept termed pseudogradient (PG). Moreover, the predictive function is implemented in the controller using a lazy-learning (LL)-based PG predictive algorithm, such that the controller not only shows good robustness but also can realize the effect of model-free adaptive prediction for the sudden change of the desired signal. Further, since the LL technique has the characteristic of database queries, both the online and offline I/O measurement data are fully and simultaneously utilized to real-time adjust the controller parameters during the control process. Moreover, the stability of the proposed method is guaranteed by rigorous mathematical analysis. Meanwhile, the numerical simulations and the laboratory experiments implemented on a practical three-tank water level control system both verify the effectiveness of the proposed approach.
Long term memory (LTM) in climate variability is studied by means of fractional integral techniques. By using a recently developed model, Fractional Integral Statistical Model (FISM), we in this report proposed a new method, with which one can estimate the long-lasting influences of historical climate states on the present time quantitatively, and further extract the influence as climate memory signals. To show the usability of this method, two examples, the Northern Hemisphere monthly Temperature Anomalies (NHTA) and the Pacific Decadal Oscillation index (PDO), are analyzed in this study. We find the climate memory signals indeed can be extracted and the whole variations can be further decomposed into two parts: the cumulative climate memory (CCM) and the weather-scale excitation (WSE). The stronger LTM is, the larger proportion the climate memory signals will account for in the whole variations. With the climate memory signals extracted, one can at least determine on what basis the considered time series will continue to change. Therefore, this report provides a new perspective on climate prediction.
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