Kinetic studies on the dimerization of isobutene with Ni/Al_2O_3 as a catalyst for reactive distillation process

Isooctane is a promising gasoline additive that could be produced by dimerization of isobutene(IB) with subsequent hydrogenation.In this work,the dimerization of IB has been carried out in a batch reactor over a temperature range of 338-383 K in the presence of laboratory prepared Ni/Al_2O_3 as a catalyst and n-pentane as solvent.The influence of various parameters such as temperature,catalyst loading and initial concentration of IB was examined.A Langmuir-Hinshelwood kinetic model of IB dimerization was established and the parameters were estimated on the basis of the measured data.The feasibility of oligomerization of IB based on the reactive distillation was simulated in ASPEN PLUS using the kinetics developed.The simulation results showed that the catalyst of Ni/Al_2O_3 had higher selectivity to diisobutene(DIB) and slightly lower conversion of IB than ion exchange resin in the absence of polar substances.

MPC of distillation column with side reactors for methyl acetate

This paper focuses on the dynamic control of distillation column with side reactors(SRC) for methyl acetate production. To obtain the optimum integrated structure and steady state simulation, the systematic design approach based on the concept of independent reaction amount is applied to the process of SRC for methyl acetate production. In addition to the basic control loops, multi-variable model predictive control modular with methyl acetate concentration and temperature of sensitive plate is designed. Then, based on process simulation software Aspen Plus, dynamic simulation of SRC for methyl acetate production is used to verify the effectiveness of the control scheme.

Accelerating BERT inference with GPU-efficient exit prediction

BERT is a representative pre-trained language model that has drawn extensive attention for significant improvements in downstream Natural Language Processing(NLP)tasks.The complex architecture and massive parameters bring BERT competitive performance but also result in slow speed at model inference time.To speed up BERT inference,FastBERT realizes adaptive inference with an acceptable drop in accuracy based on knowledge distillation and the early-exit technique.However,many factors may limit the performance of FastBERT,such as the teacher classifier that is not knowledgeable enough,the batch size shrinkage and the redundant computation of student classifiers.To overcome these limitations,we propose a new BERT inference method with GPU-Efficient Exit Prediction(GEEP).GEEP leverages the shared exit loss to simplify the training process of FastBERT from two steps into only one step and makes the teacher classifier more knowledgeable by feeding diverse Transformer outputs to the teacher classifier.In addition,the exit layer prediction technique is proposed to utilize a GPU hash table to handle the token-level exit layer distribution and to sort test samples by predicted exit layers.In this way,GEEP can avoid batch size shrinkage and redundant computation of student classifiers.Experimental results on twelve public English and Chinese NLP datasets prove the effectiveness of the proposed approach.The source codes of GEEP will be released to the public upon paper acceptance.

Model of Raman–Nath acousto-optic diffraction

In this Letter, we discuss Raman–Nath acousto-optic diffraction, and a new model of Raman–Nath acousto-optic diffraction is presented. The model is based on the individual and simultaneous occurrences of phase-grating diffraction and the Doppler effect and optical phase modulation and photon–phonon scattering. We find that the optical phase modulation can cause temporal and spatial fluctuations of the diffracted light power escaping from the acoustic field.