Predicting the best shutdown time of a steam ethylene cracking furnace in industrial practice remains a challenge due to the complex coking process. As well known, the shutdown time of a furnace is mainly determined by coking condition of the transfer line exchangers (TLE) when naphtha or other heavy hydrocarbon feedstocks are cracked. In practice, it is difficult to measure the coke thickness in TLE through experimental method in the complex industrial situation. However, the outlet temperature of TLE (TLEOT) can indirectly characterize the coking situation in TLE since the coke accumulation in TLE has great influence on TLEOT. Thus, the TLEOT could be a critical factor in deciding when to shut down the furnace to decoke. To predict the TLEOT, a parametric model was proposed in this work, based on theoretical analysis, mathematic reduction, and parameters estimation. The feasibility of the proposed model was further checked through industrial data and good agreements between model prediction and industrial data with maximum deviation 2% were observed.
Dynamic optimization problems(DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are invalid. In this article, a technology named ranking-based mutation operator(RMO) is presented to enhance the previous differential evolution(DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.
Cracking furnace is the core device for ethylene production.In practice,multiple ethylene furnaces are usually run in parallel.The scheduling of the entire cracking furnace system has great significance when multiple feeds are simultaneously processed in multiple cracking furnaces with the changing of operating cost and yield of product.In this paper,given the requirements of both profit and energy saving in actual production process,a multi-objective optimization model contains two objectives,maximizing the average benefits and minimizing the average coking amount was proposed.The model can be abstracted as a multi-objective mixed integer nonlinear programming problem.Considering the mixed integer decision variables of this multi-objective problem,an improved hybrid encoding non-dominated sorting genetic algorithm with mixed discrete variables(MDNSGA-Ⅱ)is used to solve the Pareto optimal front of this model,the algorithm adopted crossover and mutation strategy with multi-operators,which overcomes the deficiency that normal genetic algorithm cannot handle the optimization problem with mixed variables.Finally,using an ethylene plant with multiple cracking furnaces as an example to illustrate the effectiveness of the scheduling results by comparing the optimization results of multi-objective and single objective model.
Dynamic multi-objective optimization is a complex and difficult research topic of process systems engineering. In this paper,a modified multi-objective bare-bones particle swarm optimization( MOBBPSO) algorithm is proposed that takes advantage of a few parameters of bare-bones algorithm. To avoid premature convergence,Gaussian mutation is introduced; and an adaptive sampling distribution strategy is also used to improve the exploratory capability. Moreover, a circular crowded sorting approach is adopted to improve the uniformity of the population distribution.Finally, by combining the algorithm with control vector parameterization,an approach is proposed to solve the dynamic optimization problems of chemical processes. It is proved that the new algorithm performs better compared with other classic multiobjective optimization algorithms through the results of solving three dynamic optimization problems.
