摘要：An industrial scale propylene production via oxidative dehydrogenation of propane （ODHP） in multi-tubular re- actors was modeled. Multi-tubular fixed-bed reactor used for ODHP process, employing 10000 of small diameter tubes immersed in a shell through a proper coolant flows. Herein, a theory-based pseudo-homogeneous model to describe the operation of a fixed bed reactor for the ODHP to correspondence olefln over V2O5/γ-Al203 catalyst was presented. Steady state one dimensional model has been developed to identify the operation parameters and to describe the propane and oxygen conversions, gas process and coolant temperatures, as well as other pa- rameters affecting the reactor performance such as pressure. Furthermore, the applied model showed that a double-bed multitubular reactor with intermediate air injection scheme was superior to a single-bed design due to the increasing of propylene selectivity while operating under lower oxygen partial pressures resulting in propane conversion of about 37.3%. The optimized length of the reactor needed to reach 100% conversion of the oxygen was theoretically determined. For the single-bed reactor the optimized length of 11.96 m including 0.5 m of inert section at the entrance region and for the double-bed reactor design the optimized lengths of 5.72 m for the first and 7.32 m for the second reactor were calculated. Ultimately, the use of a distributed oxygen feed with limited number of injection points indicated a significant improvement on the reactor performance in terms of propane conversion and propylene selectivity. Besides, this concept could overcome the reactor run- away temperature problem and enabled operations at the wider range of conditions to obtain enhanced propyl- ene production in an industrial scale reactor.

摘要：In order to more accurately examine developing trends in gross cargo throughput, we have modeled the probability distribution of cargo throughput. Gross cargo throughput is determined by the time spent by cargo ships in the port and the operating efficiency of handling equipment. Gross cargo throughput is the sum of all compound variables determining each aspect of cargo throughput for every cargo ship arriving at the port. Probability distribution was determined using the Wald equation. The results show that the variability of gross cargo throughput primarily depends on the different times required by different cargo ships arriving at the port. This model overcomes the shortcoming of previous models: inability to accurately determine the probability of a specific value of future gross cargo throughput. Our proposed model of cargo throughput depends on the relationship between time required by a cargo ship arriving at the port and the operational capacity of handling equipment at the port. At the same time, key factors affecting gross cargo throughput are analyzed. In order to test the efficiency of the model, the cargo volume of a port in Shandong Province was used as an example. In the case study the actual results matched our theoretical analysis.

摘要：A tool was developed to assist the cooling systems designer in designing and installing the microsprinklers and fan cooling system. The tool was developed by integrating a mathematical model into an electronic spark map in order to use the mathematical model practically. The mathematical model was developed using the designs, parameters, variables, and constant values of the microsprinklers and fans cooling system. Subsequently, an electronic spark map （decision tree） was developed, and then the mathematical model was integrated into the electronic spark map. Afterwards, C# （C Sharp） programming language was used to develop a computer system via the electronic spark map, and to make the user interface. The developed computer system assists the designer in making decisions to specify and to calculate the required discharge of cooling system pump, length and diameter of cooling system pipelines, number of cooling fans, and number of microsprinklers. Moreover, this tool calculates the capital investment and the fixed, variable, and total costs of the cooling system. However, the mathematical model of the spark map requires some input data such as： pressure and discharge of microsprinklers, and some other engineering parameters. Data of 4 cooling systems were used to carry out the model validation. The differences between actual and calculated values were determined, and the standard deviations were calculated. The coefficients of variation were between 2.25% and 4.13%.