How to make the production of methanol/DME "GREENER"-Integration of wind power with modern coal chemical industry

The urgency and necessity of alternative fuels give an impetus to the development of modern coal chemical industry. Coal-based methanol/DME is the key element of this industry. Wind power, whose installed capacity increased at a rate of more than 100% in recent years, has the most developed technologies in renewable energy. However, there still exist many unsolved problems in wind power for on-grid utilization. A new integrated system which combines coal-based methanol/DME production with wind power is proposed in this paper. In this system, wind power is used to electrolyze water to produce H₂ and O₂. TheO₂ is fed to the gasifier as gasification agent. The H₂ is mixed with the CO-rich gas to adjust the H₂/CO to an appropriate ratio for methanol synthesis. In comparison with conventional coal-based methanol/DME system, the proposed system omits the expensive and energy-consuming ASU and greatly reduces the water gas shift process, which brings both advantages in the utilization of all raw materials and significant mitigation of CO₂ emission. This system will be attractive in the regions of China which have abundant wind and coal resources.     

From probabilistic forecasts to statistical scenarios of short‐term wind power production

Short‐term (up to 2–3 days ahead) probabilistic forecasts of wind power provide forecast users with highly valuable information on the uncertainty of expected wind generation. Whatever the type of these probabilistic forecasts, they are produced on a per horizon basis, and hence do not inform on the development of the forecast uncertainty through forecast series. However, this additional information may be paramount for a large class of time‐dependent and multistage decision‐making problems, e.g. optimal operation of combined wind‐storage systems or multiple‐market trading with different gate closures. This issue is addressed here by describing a method that permits the generation of statistical scenarios of short‐term wind generation that accounts for both the interdependence structure of prediction errors and the predictive distributions of wind power production. The method is based on the conversion of series of prediction errors to a multivariate Gaussian random variable, the interdependence structure of which can then be summarized by a unique covariance matrix. Such matrix is recursively estimated in order to accommodate long‐term variations in the prediction error characteristics. The quality and interest of the methodology are demonstrated with an application to the test case of a multi‐MW wind farm over a period of more than 2 years. Copyright © 2008 John Wiley & Sons, Ltd.     

Analysis of the current methods used to size a wind/hydrogen/fuel cell‐integrated system: A new perspective

As an alternative to the production and storage of intermittent renewable energy sources, it has been suggested that one can combine several renewable energy technologies in one system, known as integrated or hybrid system, that integrate wind technology with hydrogen production unit and fuel cells. This work assesses the various methods used in sizing such systems. Most of the published papers relate the use of simulation tools such as HOMER, HYBRID2 and TRNSYS, to simulate the operation of different configurations for a given application in order to select the best economic option. But, with these methods one may not accurately determine certain characteristics of the energy resources available on a particular site, the profiles of estimated consumption and the demand for hydrogen, among other factors, which will be the optimal parameters of each subsystem. For example, velocity design, power required for the wind turbine, power required for the fuel cell and electrolyzer and the storage capacity needed for the system. Moreover, usually one makes excessive use of bi‐parametric Weibull distribution function to approximate the histogram of the observed wind to the theoretical, which is not appropriate when there are bimodal frequency distributions of wind, as is the case in several places in the world. A new perspective is addressed in this paper, based on general system theory, modeling and simulation with a systematic approach and the use of exergoeconomic analysis. There are some general ideas on the advantages offered in this method, which is meant for the implementation of wind/hydrogen/fuel cell‐integrated systems and in‐situ clean hydrogen production. Copyright © 2009 John Wiley & Sons, Ltd.     

CO<Subscript>2</Subscript> mitigation in coal gasification cogeneration systems with integration of the shift reaction,CO<Subscript>2</Subscript> absorption and methanol production

Cogeneration of electricity and liquid fuel can achieve higher efficiencies than electricity generation alone in Integrated Gasification Combined Cycle (IGCC), and cogeneration systems are also expected to mitigate CO₂ emissions. A proposed methanol-electricity cogeneration system was analyzed in this paper using exergy method to evaluate the specified system. A simple cogeneration scheme and a complicated scheme including the shift reaction and CO₂ removal were compared. The results show that the complicated scheme consumes more energy, but has a higher methanol synthesis ratio with partial capture of CO₂. In those methanol and electricity cogeneration systems, the CO₂ mitigation is not merely an additional process that consumes energy and reduces the overall efficiency, but is integrated into the methanol production.     

Direct conversion of syngas to DME over CuO–ZnO–Al2O3/HZSM‐5 nanocatalyst synthesized via ultrasound‐assisted co‐precipitation method: New insights into the role of gas injection

In this study, direct synthesis of dimethyl ether (DME) is conducted over a bifunctional CuO–ZnO–Al₂O₃/H Zeolite Socony Mobil‐5 (HZSM‐5) nanocatalyst. A hybrid method of ultrasound‐assisted co‐precipitation is used for the synthesis of catalysts, and the effect of gas injection during sonication is investigated. The physicochemical characteristics of the catalysts are analysed by X‐ray diffraction (XRD), field emission scanning electron microscopy (FESEM), particle size distribution (PSD), energy dispersive X‐ray (EDX), Brunauer–Emmett–Teller (BET) and Fourier‐transformed infrared (FTIR) methods. In the absence of gas injection, the acetate‐based catalysts have a better morphology and higher surface area than the nitrate‐based catalyst. Gas injection significantly changes the morphology and structural properties of the acetate‐based catalyst. High surface area, narrow PSD and better dispersion of small CuO crystals are obtained in a gas‐injected synthesized sample. DME synthesis experiments showed that the CO conversion and DME selectivity are correlated with surface area, nanocatalyst particle size and its dispersion. The gas‐injected CuO–ZnO–Al₂O₃/HZSM‐5 nanocatalyst that has the highest surface area and the smallest dispersed particles showed more than 70% DME selectivity. The gas‐injected CuO–ZnO–Al₂O₃/HZSM‐5 nanocatalyst exhibited high stability in terms of CO conversion and DME yield over 1440‐min time on a stream test at 275°C, 40 bar and 18 000 cm³ g.h^?1. Copyright © 2014 John Wiley & Sons, Ltd.