Microscopic mechanism study and process optimization of dimethyl carbonate production coupled biomass chemical looping gasification system

Biomass chemical looping gasification technology is one of the essential ways to utilize abundant biomass resources.At the same time,dimethyl carbonate can replace phosgene as an environmentfriendly organic material for the synthesis of polycarbonate.In this paper,a novel system coupling biomass chemical looping gasification with dimethyl carbonate synthesis with methanol as an intermediate is designed through microscopic mechanism analysis and process optimization.Firstly,reactive force field molecular dynamics simulation is performed to explore the reaction mechanism of biomass chemical looping gasification to determine the optimal gasification temperature range.Secondly,steady-state simulations of the process based on molecular dynamics simulation results are carried out to investigate the effects of temperature,steam to biomass ratio,and oxygen carrier to biomass ratio on the syngas yield and compositions.In addition,the main energy indicators of biomass chemical looping gasification process including lower heating value and cold gas efficiency are analyzed based on the above optimum parameters.Then,two synthesis stages are simulated and optimized with the following results obtained:the optimal temperature and pressure of methanol synthesis stage are 150℃ and 4 MPa;the optimal temperature and pressure of dimethyl carbonate synthesis stage are 140℃ and 0.3 MPa.Finally,the pre-separation-extraction-decantation process separates the mixture of dimethyl carbonate and methanol generated in the synthesis stage with 99.11%purity of dimethyl carbonate.Above results verify the feasibility of producing dimethyl carbonate from the perspective of multi-scale simulation and realize the multi-level utilization of biomass resources.

Fault monitoring based on mutual information feature engineering modeling in chemical process

A large amount of information is frequently encountered when characterizing the sample model in chemical process.A fault diagnosis method based on dynamic modeling of feature engineering is proposed to effectively remove the nonlinear correlation redundancy of chemical process in this paper.From the whole process point of view,the method makes use of the characteristic of mutual information to select the optimal variable subset.It extracts the correlation among variables in the whitening process without limiting to only linear correlations.Further,PCA(Principal Component Analysis)dimension reduction is used to extract feature subset before fault diagnosis.The application results of the TE(Tennessee Eastman)simulation process show that the dynamic modeling process of MIFE(Mutual Information Feature Engineering)can accurately extract the nonlinear correlation relationship among process variables and can effectively reduce the dimension of feature detection in process monitoring.

PCA weight and Johnson transformation based alarm threshold optimization in chemical processes

To alleviate the heavy load of massive alarm on operators, alarm threshold in chemical processes was optimized with principal component analysis(PCA) weight and Johnson transformation in this paper. First, few variables that have high PCA weight factors are chosen as key variables. Given a total alarm frequency to these variables initially, the allowed alarm number for each variable is determined according to their sampling time and weight factors. Their alarm threshold and then control limit percentage are determined successively. The control limit percentage of non-key variables is determined with 3σ method alternatively. Second, raw data are transformed into normal distribution data with Johnson function for all variables before updating their alarm thresholds via inverse transformation of obtained control limit percentage. Alarm thresholds are optimized by iterating this process until the calculated alarm frequency reaches standard level(normally one alarm per minute). Finally,variables and their alarm thresholds are visualized in parallel coordinate to depict their variation trends concisely and clearly. Case studies on a simulated industrial atmospheric-vacuum crude distillation demonstrate that the proposed alarm threshold optimization strategy can effectively reduce false alarm rate in chemical processes.

Identification of abnormal conditions in high-dimensional chemical process based on feature selection and deep learning

Identification of abnormal conditions is essential in the chemical process.With the rapid development of artificial intelligence technology,deep learning has attracted a lot of attention as a promising fault identification method in chemical process recently.In the high-dimensional data identification using deep neural networks,problems such as insufficient data and missing data,measurement noise,redundant variables,and high coupling of data are often encountered.To tackle these problems,a feature based deep belief networks(DBN)method is proposed in this paper.First,a generative adversarial network(GAN)is used to reconstruct the random and non-random missing data of chemical process.Second,the feature variables are selected by Spearman’s rank correlation coefficient(SRCC)from high-dimensional data to eliminate the noise and redundant variables and,as a consequence,compress data dimension of chemical process.Finally,the feature filtered data is deeply abstracted,learned and tuned by DBN for multi-case fault identification.The application in the Tennessee Eastman(TE)process demonstrates the fast convergence and high accuracy of this proposal in identifying abnormal conditions for chemical process,compared with the traditional fault identification algorithms.

Mechanism analysis and simulation of methyl methacrylate production coupled chemical looping gasification system

Nowadays,the efficient and cleaner utilization of coal have attracted wide attention due to the rich coal and rare oil/gas resources structure in China.Coal chemical looping gasification(CCLG)is a promising coal utilization technology to achieve energy conservation and emission reduction targets for highly pure synthesis gas.As a downstream product of synthesis gas,methyl methacrylate(MMA),is widely used as raw material for synthesizing polymethyl methacrylate and resin products with excellent properties.So this paper proposes a novel system integrating MMA production and CCLG(CCLG-MMA)processes aiming at"energy saving and low emission",in which the synthesis gas produced by CCLG and purified by dry methane reforming(DMR)reaction and Rectisol process reacts with ethylene for synthesizing MMA.Firstly,the reaction mechanism of CCLG is investigated by using Reactive force field(ReaxFF)MD simulation based on atomic models of char and oxygen carrier(Fe_(2)O_(3))for obtaining optimum reaction temperature of fuel reactor(FR).Secondly,the steady-state simulation of CCLG-MMA system is carried out to verify the feasibility of MMA production.The amount of CO_(2)emitted by CCLG process and DMR reaction is 0.0028(kg CO_(2))^(-1)·(kg MMA)^(-1).The total energy consumption of the CCLG-MMA system is 45521 kJ·(kg MMA)^(-1),among which the consumption of MMA production part is 25293 k(·kg MMA)^(-1).The results show that the CCLG-MMA system meets CO_(2)emission standard and has lower energy consumption compared to conventional MMA production process.Finally,one control scheme is designed to verify the stability of CCLG-MMA system.The CCLG-MMA integration strategy aims to obtain highly pure MMA from multi-scale simulation perspectives,so this is an optimal design regarding all factors influencing cleaner MMA production.