Coal is the most abundant fossil fuel known in China,and also a vital global energy source.Today,the main consumption of coal is pulverized coal combustion for power generation which leads to environmental pollution and wastes.Direct coal liquefaction is an advantageous approach for the clean and effective utilization of coal,and also an effective way to solve the energy shortage problem in our country.For the successful process design and its engineering of coal liquefaction,it is inevitable to implement the liquefaction modeling study,i.e.,exploring the reaction schemes involved,deriving the kinetics rate expressions and identifying the major influencial process parameters,so as to quantitatively describe the complicated reaction process taking place.This research was performed with a partial support of the National Basic Research Program(973 Program)of China,aiming at the modeling and simulations of the kinetics of Shenhua coal direct liquefaction carried out in batch reactors.The objectives of the present work are 1)to develop the liquefaction kinetics model for Shenhua coal by incorporating thermodynamic calculations,and 2)to build reactor models of direct coal liquefaction by using artificial neural networks(ANN)and support vector machine(SVM)for simulating the performances of batch reactors, respectively.First,a batch reactor model for Shenhua coal liquefaction was developed,taking the co-existing vapor and liquid phases into account. Based on the kinetics data reported in literature,we derived quantitatively the reaction kinetics of Shenhua coal liquefaction.It is demonstrated that it is necessary in the interpretation of the kinetics data to calculate the concentrations of each component present in the liquid phase by thermodynamic calculations.The coal was divided into three parts,i.e., easy reactive part,hard reactive part and unreactive part.The easy reactive part generates directly the oil plus gas(O+G);the hard reactive part generates the preasphaltene(P).The counterreaction from P to(O+G)is inoperative,however the counterreaction from asphaltene(A)to P will response at high tempretures.Under lower tempretures,the conversion of P to A is the rate limited step for coal liquefaction,while at a high tempreture, the conversion of A to(O+G)is dominant.The simulation results of artificial neural networks(ANN)reactor model and support vector machine(SVM)for batch reactors show that, while the elective range of coal and solvent is narrow,the most influencial factor in coal liquefaction is tempreture;at the same time,the type and particle of the coal is assignable cause for oil yield.When the system researched is extended into eleven influencing factors and wide factor range,Coal variables(coal type and particle size)are the most important factors in the coal liquefaction system.In addition,particle size, temperature and gas pressure have a profound influence on the result of coal conversion,to the contrary,solvent type is a more important factor toward oil yield than coal conversion,those four factors(degree of fill, mixing,reaction time and solvent/coal ratio)have the great effect on coal conversion and oil yield.Compared with other factors,heat-up time and drying plays a less important role in liquefaction process.