Smart Process Manufacturing toward Carbon Neutrality:Digital Transformation in Process Manufacturing for Achieving the Goals of Carbon Peak and Carbon Neutrality

At present,the energy-supply and process-manufacturing industries suffer from the outstanding problems of heavy industrial structure,high energy consumption,and high carbonization of the energy supply,with the global demand and consumption of energy continuing to grow.Pollution emissions from high-carbon energy systems are significantly accelerating the global climate crisis.Therefore,there is urgent need for an energy revolution and industrial transformation in these industries.The digitalization of process manufacturing by relying on big data and artificial intelligence can effectively help process-manufacturing companies improve their efficiency and energy conservation.To achieve the goals of a carbon peak and carbon neutrality,innovative digital technology and its application in the fields of pharmaceuticals,chemicals.

Phosphotungstic acid immobilized on amino-functionalized TS-1 zeolite as a solid acid catalyst for the synthesis of tributyl citrate

The amino-functionalization of TS-1 zeolite followed by immobilization of phosphotungstic acid(HPW)was presented to prepare a strong solid acid catalyst for the synthesis of bio-based tributyl citrate from the esterification of citric acid and n-butanol.γ-Aminopropyltriethoxysilane(APTES)was first grafted on the TS-1 zeolite via the condensation reactions with surface hydroxyl groups,and subsequently the HPW was immobilized via the reaction between the amino groups and the protons from HPW-forming strong ionic bonding.The Keggin structure of HPW and MFI topology of TS-1 zeolite were well maintained after the modifications.The amino-functionalization generated abundant uniformly distributed active sites on TS-1 for HPW immobilization,which promoted the dispersity,abundance,as well as the stability of the acid sites.The tetrahedrally coordinated framework titanium and non-framework titania behaved as weak Lewis acid sites,and the protons from the immobilized HPW acted as the moderate or strong Brønsted acid sites.An optimized TBC yield of 96.2%(mol)with a conversion of-COOH of 98.1%(mol)was achieved at 150℃for 6 h over the HPW immobilized on amino-functionalized TS-1.The catalyst exhibited good stability after four consecutive reaction runs,where the activity leveled off at still a relatively high level after somewhat deactivation possibly caused by the leaching of a small portion of weakly anchored APTES or HPW.

Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process

Gasoline blending scheduling optimization can bring significant economic and efficient benefits to refineries.However,the optimization model is complex and difficult to build,which is a typical mixed integer nonlinear programming(MINLP)problem.Considering the large scale of the MINLP model,in order to improve the efficiency of the solution,the mixed integer linear programming-nonlinear programming(MILP-NLP)strategy is used to solve the problem.This paper uses the linear blending rules plus the blending effect correction to build the gasoline blending model,and a relaxed MILP model is constructed on this basis.The particle swarm optimization algorithm with niche technology(NPSO)is proposed to optimize the solution,and the high-precision soft-sensor method is used to calculate the deviation of gasoline attributes,the blending effect is dynamically corrected to ensure the accuracy of the blending effect and optimization results,thus forming a prediction-verification-reprediction closed-loop scheduling optimization strategy suitable for engineering applications.The optimization result of the MILP model provides a good initial point.By fixing the integer variables to the MILPoptimal value,the approximate MINLP optimal solution can be obtained through a NLP solution.The above solution strategy has been successfully applied to the actual gasoline production case of a refinery(3.5 million tons per year),and the results show that the strategy is effective and feasible.The optimization results based on the closed-loop scheduling optimization strategy have higher reliability.Compared with the standard particle swarm optimization algorithm,NPSO algorithm improves the optimization ability and efficiency to a certain extent,effectively reduces the blending cost while ensuring the convergence speed.

Effects of zinc on χ-Fe_(5)C_(2) for carbon dioxide hydrogenation to olefins:Insights from experimental and density function theory calculations

Production of light olefins from CO_(2), the primary greenhouse gases, is of great importance to mitigate the adverse effects of CO_(2) emission on environment and to supply the value-added products from nonpetroleum resource. However, development of robust catalyst with controllable selectivity and stability remains a challenge. Herein, we report that Zn-promoted Fe catalyst can boost the stable and selective production of light olefins from CO_(2). Specifically, the Zn-promoted Fe exhibits a highly stable activity and olefin selectivity over 200 h time-on-stream compared to the unpromoted Fe catalyst, primarily owing to the preservation of active χ-Fe_(5)C_(2) phase. Structural characterizations of the spent catalysts suggest that Zn substantially regulates the content of iron carbide on the surface and suppresses the reoxidation of bulk iron carbide during the reaction. DFT calculations confirm that adsorption of surface carbon atoms and graphene-like carbonaceous species are not thermochemically favored on Zn-promoted Fe catalyst. Carbon deposition by CAC coupling reactions of two surface carbon atoms and dehydrogenation of CH intermediate are also inhibited. Furthermore, the effects of Zn on antioxidation of iron carbide were also investigated. Zn favored the hydrogenation of surface adsorbed oxygen atoms to H_(2)O and the desorption of H_(2)O, which reduces the possibility of surface carbide being oxidized by the chemisorbed oxygen.

Optimization of sensor deployment sequences for hazardous gas leakage monitoring and source term estimation

Nowadays, chemical safety has attracted considerable attention, and chemical gas leakage monitoring and source term estimation(STE) have become hot spots. However, few studies have focused on sensor layouts in scenarios with multiple potential leakage sources and wind conditions, and studies on the risk information(RI) detection and prioritization order of sensors have not been performed. In this work, the monitoring area of a chemical factory is divided into multiple rectangles with a uniform mesh. The RI value of each grid node is calculated on the basis of the occurrence probability and normalized concentrations of each leakage scenario. A high RI value indicates that a sensor at a grid node has a high chance of detecting gas concentrations in different leakage scenarios. This situation is beneficial for leakage monitoring and STE. The methods of similarity redundancy detection and the maximization of sensor RI detection are applied to determine the sequence of sensor locations. This study reveals that the RI detection of the optimal sensor layout with eight sensors exceeds that of the typical layout with 12 sensors. In addition, STE with the optimized placement sequence of the sensor layout is numerically simulated. The statistical results of each scenario with various numbers of sensors reveal that STE is affected by sensor number and scenarios(leakage locations and winds). In most scenarios, appropriate STE results can be retained under the optimal sensor layout even with four sensors. Eight or more sensors are advised to improve the performance of STE in all scenarios. Moreover, the reliability of the STE results in each scenario can be known in advance with a specific number of sensors. Such information thus provides a reference for emergency rescue.

Data-Driven Fault Compensation Tracking Control for Coupled Wastewater Treatment Process

Dear Editor, This letter is concerned with the data-driven fault compensation tracking control for a coupled wastewater treatment process(WWTP)subject to sensor faults. Invariant set theory is introduced to eliminate the completely bounded and differentiable conditions of coupled non-affine dynamics and to explicitly express the control inputs.An adaptive fault compensation mechanism is constructed to accommodate the effects of sensor faults. By employing a cubic absolutevalue Lyapunov criteria。

Editorial for Special Issue“Artificial Intelligence Energizes Process Manufacturing”

Process manufacturing is a pillar of modern economy;it is the dominant mode of production in many industries,including oil and gas,chemicals,nonferrous metals,iron,steel,and more.In order to address the problems of resource constraints,energy efficiency,and environmental protection in process manufacturing,it is necessary to develop systems and methods to make process manufacturing more efficient,greener,and smarter.From another perspective,artificial intelligence has been successfully applied in various fields,such as autonomous vehicles,image analysis,robotic manipulators,real-time assistants,and smart recommendation,and has demonstrated its powerful strengths in knowledge representation,cognitive comprehension,and autonomous learning.Therefore,a deep and tight integration between artificial intelligence and process manufacturing is a promising direction toward“smart process manufacturing.”Smart process manufacturing has become a hot research topic in recent years,and various governments have released strategic plans for smart process manufacturing with the aim of upgrading and transforming the process industry.

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.

Data-driven Wasserstein distributionally robust chance-constrained optimization for crude oil scheduling under uncertainty

Crude oil scheduling optimization is an effective method to enhance the economic benefits of oil refining.But uncertainties,including uncertain demands of crude distillation units(CDUs),might make the production plans made by the traditional deterministic optimization models infeasible.A data-driven Wasserstein distributionally robust chance-constrained(WDRCC)optimization approach is proposed in this paper to deal with demand uncertainty in crude oil scheduling.First,a new deterministic crude oil scheduling optimization model is developed as the basis of this approach.The Wasserstein distance is then used to build ambiguity sets from historical data to describe the possible realizations of probability distributions of uncertain demands.A cross-validation method is advanced to choose suitable radii for these ambiguity sets.The deterministic model is reformulated as a WDRCC optimization model for crude oil scheduling to guarantee the demand constraints hold with a desired high probability even in the worst situation in ambiguity sets.The proposed WDRCC model is transferred into an equivalent conditional value-at-risk representation and further derived as a mixed-integer nonlinear programming counterpart.Industrial case studies from a real-world refinery are conducted to show the effectiveness of the proposed method.Out-of-sample tests demonstrate that the solution of the WDRCC model is more robust than those of the deterministic model and the chance-constrained model.

Distributed Optimal Variational GNE Seeking in Merely Monotone Games

In this paper, the optimal variational generalized Nash equilibrium(v-GNE) seeking problem in merely monotone games with linearly coupled cost functions is investigated, in which the feasible strategy domain of each agent is coupled through an affine constraint. A distributed algorithm based on the hybrid steepest descent method is first proposed to seek the optimal v-GNE. Then, an accelerated algorithm with relaxation is proposed and analyzed, which has the potential to further improve the convergence speed to the optimal v-GNE. Some sufficient conditions in both algorithms are obtained to ensure the global convergence towards the optimal v-GNE. To illustrate the performance of the algorithms, numerical simulation is conducted based on a networked Nash-Cournot game with bounded market capacities.

Visual Semantic Segmentation Based on Few/Zero-Shot Learning:An Overview

Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block,and it plays a crucial role in environmental perception.Conventional learning-based visual semantic segmentation approaches count heavily on largescale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories.This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning.The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen categories from a few labeled or even zero-labeled samples,which advances the extension to practical applications.Therefore,this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances.Specifically,the preliminaries on few/zeroshot visual semantic segmentation,including the problem definitions,typical datasets,and technical remedies,are briefly reviewed and discussed.Moreover,three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation,including image semantic segmentation,video object segmentation,and 3D segmentation.Finally,the future challenges of few/zero-shot visual semantic segmentation are discussed.

Modeling and Optimization of a Fractionation,Absorption,and Stabilization System in an Industrial Fluid Catalytic Cracking Process

Fluid catalytic cracking(FCC)is a vitally important refinery process.The fractionation,absorption,and stabilization system in the FCC process is a significant way to obtain key products,and its parameters will directly affect the quality of the products.In this work,using industrial data from an actual FCC process,a model of the FCC fractionation,absorption,and stabilization system was developed using process simulation software.The sequence quadratic program algorithm was then used to identify the parameters of each tower,increasing the accuracy of the simulation results.Next,using this improved model,a sensitivity analysis was performed to examine the effects of different operating conditions.The pattern-search method was then used to optimize the operating parameters of the system.The results showed that the optimized model has good prediction accuracy,and using the model,it was found that changing the operation parameters could result in a 1.84%improvement in economic benefits.As such,the developed model was demonstrated to be usefully applicable to the optimization of the process operation of an FCC fractionation,absorption,and stabilization system.

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.

A Brief Overview of ChatGPT:The History,Status Quo and Potential Future Development

ChatG PT,an artificial intelligence generated content (AIGC) model developed by OpenAI,has attracted worldwide attention for its capability of dealing with challenging language understanding and generation tasks in the form of conversations.This paper briefly provides an overview on the history,status quo and potential future development of ChatGPT,helping to provide an entry point to think about ChatGPT.Specifically,from the limited open-accessed resources,we conclude the core techniques of ChatGPT,mainly including large-scale language models,in-context learning,reinforcement learning from human feedback and the key technical steps for developing ChatGPT.We further analyze the pros and cons of ChatGPT and we rethink the duality of ChatGPT in various fields.Although it has been widely acknowledged that ChatGPT brings plenty of opportunities for various fields,mankind should still treat and use ChatG PT properly to avoid the potential threat,e.g.,academic integrity and safety challenge.Finally,we discuss several open problems as the potential development of ChatGPT.

A Local Quadratic Embedding Learning Algorithm and Applications for Soft Sensing

Inspired by the tremendous achievements of meta-learning in various fields,this paper proposes the local quadratic embedding learning(LQEL)algorithm for regression problems based on metric learning and neural networks(NNs).First,Mahalanobis metric learning is improved by optimizing the global consistency of the metrics between instances in the input and output space.Then,we further prove that the improved metric learning problem is equivalent to a convex programming problem by relaxing the constraints.Based on the hypothesis of local quadratic interpolation,the algorithm introduces two lightweight NNs;one is used to learn the coefficient matrix in the local quadratic model,and the other is implemented for weight assignment for the prediction results obtained from different local neighbors.Finally,the two sub-mod els are embedded in a unified regression framework,and the parameters are learned by means of a stochastic gradient descent(SGD)algorithm.The proposed algorithm can make full use of the information implied in target labels to find more reliable reference instances.Moreover,it prevents the model degradation caused by sensor drift and unmeasurable variables by modeling variable differences with the LQEL algorithm.Simulation results on multiple benchmark datasets and two practical industrial applications show that the proposed method outperforms several popular regression methods.

Hierarchical multihead self-attention for time-series-based fault diagnosis

Fault diagnosis is important for maintaining the safety and effectiveness of chemical process.Considering the multivariate,nonlinear,and dynamic characteristic of chemical process,many time-series-based data-driven fault diagnosis methods have been developed in recent years.However,the existing methods have the problem of long-term dependency and are difficult to train due to the sequential way of training.To overcome these problems,a novel fault diagnosis method based on time-series and the hierarchical multihead self-attention(HMSAN)is proposed for chemical process.First,a sliding window strategy is adopted to construct the normalized time-series dataset.Second,the HMSAN is developed to extract the time-relevant features from the time-series process data.It improves the basic self-attention model in both width and depth.With the multihead structure,the HMSAN can pay attention to different aspects of the complicated chemical process and obtain the global dynamic features.However,the multiple heads in parallel lead to redundant information,which cannot improve the diagnosis performance.With the hierarchical structure,the redundant information is reduced and the deep local time-related features are further extracted.Besides,a novel many-to-one training strategy is introduced for HMSAN to simplify the training procedure and capture the long-term dependency.Finally,the effectiveness of the proposed method is demonstrated by two chemical cases.The experimental results show that the proposed method achieves a great performance on time-series industrial data and outperforms the state-of-the-art approaches.

A Linear Algorithm for Quantized Event-Triggered Optimization Over Directed Networks

Dear Editor,This letter investigates a class of distributed optimization problems with constrained communication.A quantized discrete-time eventtriggered zero-gradient-sum algorithm(QDE-ZGS)is developed to optimize the sum of local functions over weight-balanced directed networks.Based on an encoder-decoder scheme and a zooming-in technique,an event-triggered quantization communication is designed.Theoretical analysis shows that the exact convergence to the global optimal solution is guaranteed when the triggering threshold is bounded and the scaled sequence introduced by the zooming-in technique is quadratic summable.When the scaled sequence is bounded by an exponential decay function,QDE-ZGS converges linearly to the unique global optimal solution.Numerical simulations are conducted to demonstrate the theoretical results.

A home energy management approach using decoupling value and policy in reinforcement learning

Considering the popularity of electric vehicles and the flexibility of household appliances,it is feasible to dispatch energy in home energy systems under dynamic electricity prices to optimize electricity cost and comfort residents.In this paper,a novel home energy management(HEM)approach is proposed based on a data-driven deep reinforcement learning method.First,to reveal the multiple uncertain factors affecting the charging behavior of electric vehicles(EVs),an improved mathematical model integrating driver’s experience,unexpected events,and traffic conditions is introduced to describe the dynamic energy demand of EVs in home energy systems.Second,a decoupled advantage actor-critic(DA2C)algorithm is presented to enhance the energy optimization performance by alleviating the overfitting problem caused by the shared policy and value networks.Furthermore,separate networks for the policy and value functions ensure the generalization of the proposed method in unseen scenarios.Finally,comprehensive experiments are carried out to compare the proposed approach with existing methods,and the results show that the proposed method can optimize electricity cost and consider the residential comfort level in different scenarios.

流程工业智能制造的未来

Manufacturing processes in the process industry comprise a process branch that uses petroleum,natural gas,coal,ore,biomass,and other resources as key raw materials;provides bulk raw materials for national economic construction;and provides high-end electronic chemicals,polymers,and alloy steels for strategic emerging industries,such as the integrated circuit industry and aerospace industry,and for key technologies,such as 5G technology and high-end equipment.These manufacturing processes involve physical and chemical reactions,gas–liquid–solid multiphase coexistence,and continuous complex material-conversion processes.Process industries include the petrochemical,chemical,steel,nonferrous metal,building-material,biomedicine,and other industries,whose industry chain is shown in Fig.1.

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.