A deep reinforcement learning approach to gasoline blending real-time optimization under uncertainty

The gasoline inline blending process has widely used real-time optimization techniques to achieve optimization objectives,such as minimizing the cost of production.However,the effectiveness of real-time optimization in gasoline blending relies on accurate blending models and is challenged by stochastic disturbances.Thus,we propose a real-time optimization algorithm based on the soft actor-critic(SAC)deep reinforcement learning strategy to optimize gasoline blending without relying on a single blending model and to be robust against disturbances.Our approach constructs the environment using nonlinear blending models and feedstocks with disturbances.The algorithm incorporates the Lagrange multiplier and path constraints in reward design to manage sparse product constraints.Carefully abstracted states facilitate algorithm convergence,and the normalized action vector in each optimization period allows the agent to generalize to some extent across different target production scenarios.Through these well-designed components,the algorithm based on the SAC outperforms real-time optimization methods based on either nonlinear or linear programming.It even demonstrates comparable performance with the time-horizon based real-time optimization method,which requires knowledge of uncertainty models,confirming its capability to handle uncertainty without accurate models.Our simulation illustrates a promising approach to free real-time optimization of the gasoline blending process from uncertainty models that are difficult to acquire in practice.

A stochastic reconstruction strategy based on a stratified library of structural descriptors and its application in the molecular reconstruction of naphtha

Molecular reconstruction is a rapid and reliable way to provide molecular detail of petroleum fractions,which is required in the kinetic modeling of petroleum conversation processes at the molecular level.In the typical stochastic reconstruction method,the estimation of properties of pseudo molecules that are generated by Monte Carlo sampling depends on the building of predefined molecular libraries,which is expensive and inaccessible for certain petroleum fractions.In this paper,a novel stochastic reconstruction strategy is proposed,which is based on a stratified library of structural descriptors.Properties of pseudo molecules generated in the novel strategy can be directly estimated by group contribution method in the condition of lacking predefined molecular libraries.In this strategy,the molecular building diagram comprises two steps.First,the ring structure is configured by determining the number of rings.Different from the length of chain adopted in the traditional stochastic reconstruction method,in the second step,number of structural descriptors(SDs)for binding site and chain were determined sequentially for the configuration of binding site and saturated acyclic hydrocarbon chain.These structural descriptors for binding site and chain were selected from group contribution methods.To count the number of partial overlapping sections between structural descriptors for chain,two supplementary structural descriptors were created.All possible saturated structures of hydrocarbon chains can be represented by structural descriptors at the scale of property estimation.This strategy separates the building of a predefined molecule library from the stochastic reconstruction process.The exact structures of pseudo molecules represented by structural descriptors in this work can be determined with sufficient chemical knowledge.Fifty naphtha samples are tested independently to demonstrate the performance of the proposed strategy and the results show that the estimated properties were close enough to the experimental values.This strategy will benefit the molecular management of petrochemical industries and therefore improve economic and environmental efficiencies.

Multiple input self-organizing-map ResNet model for optimization of petroleum refinery conversion units

This work introduces a deep-learning network,i.e.,multi-input self-organizing-map ResNet(MISR),for modeling refining units comprised of two reactors and a separation train.The model is comprised of self-organizing-map and the neural network parts.The self-organizing-map part maps the input data into multiple two-dimensional planes and sends them to the neural network part.In the neural network part,residual blocks enhance the convergence and accuracy,ensuring that the structure will not be overfitted easily.Development of the MISR model of hydrocracking unit also benefits from the utilization of prior knowledge of the importance of the input variables for predicting properties of the products.The results show that the proposed MISR structure predicts more accurately the product yields and properties than the previously introduced self-organizing-map convolutional neural network model,thus leading to more accurate optimization of the hydrocracker operation.Moreover,the MISR model has smoother error convergence than the previous model.Optimal operating conditions have been determined via multi-round-particle-swarm and differential evolution algorithms.Numerical experiments show that the MISR model is suitable for modeling nonlinear conversion units which are often encountered in refining and petrochemical plants.

Graph convolutional network for axial concentration profiles prediction in simulated moving bed

The simulated moving bed(SMB)chromatographic separation is a continuous compound separation process based on the differences in adsorption capacity exhibited by distinct constituents of a mixture on the fluid phase and stationary phase.The prediction of axial concentration profiles along the beds in a unit is crucial for the operating optimization of SMB.Though the correlation shared by operating variables of SMB has an enormous impact on the operational state of the device,these correlations have been long overlooked,especially by the data-driven models.This study proposes an operating variable-based graph convolutional network(OV-GCN)to enclose the underrepresented correlations and precisely predict axial concentration profiles prediction in SMB.The OV-GCN estimates operating variables with the Spearman correlation coefficient and incorporates them in the adjacency matrix of a graph convolutional network for information propagation and feature extraction.Compared with Random Forest,K-Nearest Neighbors,Support Vector Regression,and Backpropagation Neural Network,the values of the three performance evaluation metrics,namely MAE,RMSE,and R^(2),indicate that OV-GCN has better prediction accuracy in predicting five essential aromatic compounds'axial concentration profiles of an SMB for separating p-xylene(PX).In addition,the OV-GCN method demonstrates a remarkable ability to provide high-precision and fast predictions in three industrial case studies.With the goal of simultaneously maximizing PX purity and yield,we employ the non-dominated sorting genetic algorithm-II optimization method to perform multi-objective optimization of the PX purity and yield.The outcome suggests a promising approach to extracting and representing correlations among operating variables in data-driven process modeling.

Rice In Vivo RNA Structurome Reveals RNA Secondary Structure Conservation and Divergence in Plants

RNA secondary structure plays a critical role in gene regulation. Rice （Oryza sativa） is one of the most important food crops in the world. However, RNA structure in rice has scarcely been studied. Here, we have successfully generated in vivo Structure-seq libraries in rice. We found that the structural flexibility of mRNAs might associate with the dynamics of biological function. Higher N6-methyladenosine （mSA） modification tends to have less RNA structure in 3＇ UTR, whereas GC content does not significantly affect in vivo mRNA structure to maintain efficient biological processes such as translation. Comparative analysis of RNA structurome between rice and Arabidopsis revealed that higher GC content does not lead to stronger structure and less RNA structural flexibility. Moreover, we found a weak correlation between sequence and structure conservation of the orthologs between rice and Arabidopsis. The conservation and divergence of both sequence and in vivo RNA structure corresponds to diverse and specific biological processes. Our results indicate that RNA secondary structure might offer a separate layer of selection to the sequence between monocot and dicot. Therefore, our study implies that RNA structure evolves differently in various biological processes to maintain robustness in development and adaptational flexibility during angiosperm evolution.

Development and challenges of planning and scheduling for petroleum and petrochemical production

Production planning and scheduling are becoming the core of production management,which support the decision of a petrochemical company.The optimization of production planning and scheduling is attempted by every refinery because it gains additional profit and stabilizes the daily production.The optimization problem considered in industry and academic research is of different levels of realism and complexity,thus increasing the gap.Operation research with mathematical programming is a conventional approach used to address the planning and scheduling problem.Additionally,modeling the processes,objectives,and constraints and developing the optimization algorithms are significant for industry and research.This paper introduces the perspective of production planning and scheduling from the development viewpoint.

REGγ drives Lgr5^(+) stem cells to potentiate radiation induced intestinal regeneration

Leucine-rich repeat containing G protein-coupled receptor 5(Lgr5), a marker of intestinal stem cells(ISCs), is considered to play key roles in tissue homoeostasis and regeneration after acute radiation injury. However, the activation of Lgr5 by integrated signaling pathways upon radiation remains poorly understood. Here, we show that irradiation of mice with whole-body depletion or conditional ablation of REGγ in Lgr5^(+) stem cell impairs proliferation of intestinal crypts, delaying regeneration of intestine epithelial cells. Mechanistically, REGγ enhances transcriptional activation of Lgr5 via the potentiation of both Wnt and Hippo signal pathways. TEAD4 alone or cooperates with TCF4, a transcription factor mediating Wnt signaling, to enhance the expression of Lgr5. Silencing TEAD4 drastically attenuated β-catenin/TCF4 dependent expression of Lgr5. Together, our study reveals how REGγ controls Lgr5 expression and expansion of Lgr5+stem cells in the regeneration of intestinal epithelial cells.Thus, REGγ proteasome appears to be a potential therapeutic target for radiation-induced gastrointestinal disorders.

MicroPhenoDB Associates Metagenomic Data with Pathogenic Microbes, Microbial Core Genes, and Human Disease Phenotypes

Microbes play important roles in human health and disease.The interaction between microbes and hosts is a reciprocal relationship,which remains largely under-explored.Current computational resources lack manually and consistently curated data to connect metagenomic data to pathogenic microbes,microbial core genes,and disease phenotypes.We developed the MicroPhenoDB database by manually curating and consistently integrating microbe-disease association data.MicroPhenoDB provides 5677 non-redundant associations between 1781 microbes and 542 human disease phenotypes across more than 22 human body sites.MicroPhenoDB also provides 696,934 relationships between 27,277 unique clade-specific core genes and 685 microbes.Disease phenotypes are classified and described using the Experimental Factor Ontology(EFO).A refined score model was developed to prioritize the associations based on evidential metrics.The sequence search option in MicroPhenoDB enables rapid identification of existing pathogenic microbes in samples without running the usual metagenomic data processing and assembly.MicroPhenoDB offers data browsing,searching,and visualization through user-friendly web interfaces and web service application programming interfaces.MicroPhenoDB is the first database platform to detail the relationships between pathogenic microbes,core genes,and disease phenotypes.It will accelerate metagenomic data analysis and assist studies in decoding microbes related to human diseases.MicroPhenoDB is available through http://www.liwzlab.cn/microphenodb and http://lilab2.sysu.edu.cn/microphenodb.

Cytokine storm promoting T cell exhaustion in severe COVID-19 revealed by single cell sequencing data analysis

Background:Evidence has suggested that cytokine storms may be associated with T cell exhaustion(TEX)in COVID-19.However,the interaction mechanism between cytokine storms and TEX remains unclear.Methods:With the aim of dissecting the molecular relationship of cytokine storms and TEX through single-cell RNA sequencing data analysis,we identified 14 cell types from bronchoalveolar lavage fluid of COVID-19 patients and healthy people.We observed a novel subset of severely exhausted CD8 T cells(Exh T_CD8)that co-expressed multiple inhibitory receptors,and two macrophage subclasses that were the main source of cytokine storms in bronchoalveolar.Results:Correlation analysis between cytokine storm level and TEX level suggested that cytokine storms likely promoted TEX in severe COVID-19.Cell–cell communication analysis indicated that cytokines(e.g.CXCL10,CXCL11,CXCL2,CCL2,and CCL3)released by macrophages acted as ligands and significantly interacted with inhibitory receptors(e.g.CXCR3,DPP4,CCR1,CCR2,and CCR5)expressed by Exh T_CD8.These interactions formed the cytokine–receptor axes,which were also verified to be significantly correlated with cytokine storms and TEX in lung squamous cell carcinoma.Conclusions:Cytokine storms may promote TEX through cytokine-receptor axes and be associated with poor prognosis in COVID19.Blocking cytokine-receptor axes may reverse TEX.Our finding provides novel insights into TEX in COVID-19 and new clues for cytokine-targeted immunotherapy development.