Multi-objective modeling and optimization for scheduling of cracking furnace systems

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 non- linear 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 muta- tion 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.

Application of artificial intelligence in predicting the dynamics of bottom hole pressure for under-balanced drilling:Extra tree compared with feed forward neural network model

This study used six fields data alongside correlation heat map to evaluate the field parameters that affect the accuracy of bottom hole pressure(BHP)estimation.The six oil field data were acquired using measurement while drilling device to collect surface measurements of the downhole pressure data while drilling.For the two case studies,measured field data of the wellbore filled with gasified mud system was utilized,and the wellbores were drilled using rotary jointed drill strings.Extremely Randomized Tree and feed forward neural network algorithms were used to develop models that can predict with high accuracy,BHP from measured field data.For modeling purpose,an extensive data from six fields was used,and the proposed model was further validated with two data from two new fields.The gathered data encompasses a variety of well data,general information/data,depths,hole size,and depths.The developed model was compared with data obtained from two new fields based on its capability,stability and accuracy.The result and model’s performance from the error analysis revealed that the two proposed Extra Tree and Feed Forward models replicate the bottom hole pressure data with R2 greater than 0.9.The high values of R^(2) for the two models suggest the relative reliability of the modelling techniques.The magnitudes of mean squared error and mean absolute percentage error for the predicted BHPs from both models range from 0.33 to 0.34 and 2.02%-2.14%,for the Extra tree model and 0.40-0.41 and 3.90%e3.99%for Feed Forward model respectively;the least errors were recorded for the Extra Tree model.Also,the mean absolute error of the Extra Tree model for both fields(9.13-10.39 psi)are lower than that of the Feed Forward model(10.98-11 psi),thus showing the higher precision of the Extra Tree model relative to the Feed Forward model.Literature has shown that underbalanced operation does not guarantee the improvement of horizontal well’s extension ability,because it mainly depends on the relationship between the bottomhole pressure and its corresponding critical point.Thus,the application of this study proposed models for predicting bottomhole pressure trends.