A novel rotating packed bed (RPB) reactor is first adopted to intensify the reaction of isobutane alkylation with 2-butene catalyzed by H2SO4. This work investigated reaction performance, and reaction conditions were optimized. Under the optimal conditions, the research octane number (RON) reached 98.85. Meanwhile, the yields of C8 and trimethylpentanes were 90.65% and 85.47%, respectively. The reaction efficiency was tremendously improved by using RPB due to its high efficiencies of mass transfer and micromixing. More importantly, the inner mole ratio of isobutane to 2-butene was dramatically decreased in RPB, which means the energy cost of material cycling in the alkylation process could be extremely reduced. Moreover, an empirical correlation model was proposed to predict the multiproduct yields and RON with a deviation within ±10%. In conclusion, RPB reactor is a highly promising industrial platform for the process of H2SO4 alkylation.

In this work, an artificial neural network was first achieved and optimized for evaluating product distribution and studying the octane number of the sulfuric acid-catalyzed C4 alkylation process in the stirred tank and rotating packed bed. The feedstock compositions, operating conditions, and reactor types were considered as input parameters into the artificial neural network model. Algorithm, transfer function, and framework were investigated to select the optimal artificial neural network model. The optimal artificial neural network model was confirmed as a network topology of 10-20-30-5 with Bayesian Regularization backpropagation and tan-sigmoid transfer function. Research octane number and product distribution were specified as output parameters. The artificial neural network model was examined, and 5.8 × 10 –4 training mean square error, 8.66 × 10 –3 testing mean square error, and ±22% deviation were obtained. The correlation coefficient was 0.9997, and the standard deviation of error was 0.5592. Parameter analysis of the artificial neural network model was employed to investigate the influence of operating conditions on the research octane number and product distribution. It displays a bright prospect for evaluating complex systems with an artificial neural network model in different reactors.

The isobutane/butene alkylation process catalyzed by an acid solution is widely used to obtain high octane number gasoline components. However, limited by the mass transfer and mixing of the acid solution and the hydrocarbon liquid-liquid system, the product performance needs to be improved.

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With access to a high proportion of distributed photovoltaics, the power quality and line loss of distribution networks have become the focus of power grid enterprises. Traditional line loss management does not consider the impact of distributed photovoltaic access, which leads to incomplete line loss calculation methods and management means of distribution network. Therefore, this paper establishes a simulation analysis model of high proportion distributed generation connected to the distribution network and puts forward the overall loss method of the distribution network through theoretical analysis. On this basis, the IEEE 34-node system is used to calculate the impact of different capacities and locations of distributed access on the voltage distribution and line loss of the entire distribution network. The example shows that there is a corresponding relationship between the access capacity of DG and the lowest network loss. When the access capacity of distributed generation is too large, the system’s network loss will increase. Therefore, it is necessary to reasonably select the access capacity of distributed generation. The method proposed in this paper is accurate, and can also take the actual limitations of existing photovoltaic access and developable photovoltaic capacity into account, so it has strong engineering practicability.