Dual-radiation-chamber coordinated overall energy efficiency scheduling solution for ethylene cracking process regarding multi-parameter setting and multi-flow allocation

Ethylene cracking process is the core production process in ethylene industry,and is paid more attention to reduce high energy consumption.Because of the interdependent relationships between multi-flow allocation and multi-parameter setting in cracking process,it is difficult to find the overall energy efficiency scheduling for the purpose of saving energy.The traditional scheduling solutions with optimal economic benefit are not applicable for energy efficiency scheduling issue due to the neglecting of recycle and lost energy,as well as critical operation parameters as coil outlet pressure(COP)and dilution ratio.In addition,the scheduling solutions mostly regard each cracking furnace as an elementary unit,regardless of the coordinated operation of internal dual radiation chambers(DRC).Therefore,to improve energy utilization and production operation,a novel energy efficiency scheduling solution for ethylene cracking process is proposed in this paper.Specifically,steam heat recycle and exhaust heat loss are considered in cracking process based on 6 types of extreme learning machine(ELM)based cracking models incorporating DRC operation and three operation parameters as coil outlet temperature(COT),COP,and dilution ratio according to semi-mechanism analysis.Then to provide long-term decision-making basis for energy efficiency scheduling,overall energy efficiency indexes,including overall output per unit net energy input(OONE),output-input ratio per unit net energy input(ORNE),exhaust gas heat loss ratio(EGHL),are designed based on input-output analysis in terms of material and energy flows.Finally,a multiobjective evolutionary algorithm based on decomposition(MOEA/D)is employed to solve the formulated multi-objective mixed-integer nonlinear programming(MOMINLP)model.The validities of the proposed scheduling solution are illustrated through a case study.The scheduling results demonstrate that an optimal balance between multi-flow allocation,multi-parameter setting,and DRC coordinated operation is reached,which achieves 3.37%and 2.63%decreases in net energy input for same product output and conversion ratio,as well as the 1.56%decrease in energy loss ratio.

Prognostication of energy indices of tractor-implement utilizing soft computing techniques

Energy indices(energy requirement for tillage implement(ERTI)and tractor overall energy efficiency(TOEE))of tractor-implement during tillage operations were aimed to be investigated in this study.To generate a new comprehensive model,the effects of forward speed at three levels(2,4 and 6 km/h)and plowing depth at three levels(10,20 and 30 cm)on energy indices were experimentally evaluated.Two soft computing techniques,artificial neural network(ANN)and adaptive neuro-fuzzy inference system(ANFIS),were employed to prognosticate energy indices.Comparison between the best developed structure of each soft computing technique demonstrated that one comprehensive ANN model was preferred than two individual ANFIS models.According to the ANN prognostication results,simultaneous increase of forward speed from 2 to 6 km/h along with plowing depth increment from 10 to 30 cm led to nonlinear increment of the ERTI and TOEE from 33.87 to 122.66 MJ/ha and 4.65 to 17.85%,respectively.Moreover,interaction of forward speed and plowing depth on energy indices was congruent.Development of comprehensive ANN model now makes it possible to answer fundamental questions in domain of the effect of plowing depth and forward speed on energy indices of tractor-implement that were previously intractable.Hence,to properly manage energy indices and reduce energy dissipation of tractor-implement,application of the new developed ANN model is strongly recommended.