Smart Manufacturing for the Oil Refining and Petrochemical Industry

Smart manufacturing will transform the oil refining and petrochemical sector into a connected, information-driven environment. Using real-time and high-value support systems, smart manufacturing enables a coor-dinated and performance-oriented manufacturing enterprise that responds quickly to customer demandsand minimizes energy and material usage, while radically improving sustainability, productivity, innovation,and economic competitiveness. In this paper, several examples of the application of so-called ＂smart manu-facturing＂ for the petrochemical sector are demonstrated, such as the fault detection of a catalytic crackingunit driven by big data, advanced optimization for the planning and scheduling of oil refinery sites, andmore. Key scientific factors and challenges for the further smart manufacturing of chemical and petrochem-ical orocesses are identified.

Process optimization with consideration of uncertainties——An overview

Optimization under uncertainty is a challenging topic of practical importance in the Process Systems Engineering.Since the solution of an optimization problem generally exhibits high sensitivity to the parameter variations, the deterministic model which neglects the parametric uncertainties is not suitable for practical applications. This paper provides an overview of the key contributions and recent advances in the field of process optimization under uncertainty over the past ten years and discusses their advantages and limitations thoroughly. The discussion is focused on three specific research areas, namely robust optimization, stochastic programming and chance constrained programming, based on which a systematic analysis of their applications, developments and future directions are presented. It shows that the more recent trend has been to integrate different optimization methods to leverage their respective superiority and compensate for their drawbacks. Moreover, data-driven optimization, which combines mathematical programming methods and machine learning algorithms, has become an emerging and competitive tool to handle optimization problems in the presence of uncertainty based on massive historical data.

Perspectives on the flexibility analysis for continuous pharmaceutical manufacturing processes

Pharmaceutical continuous manufacturing,especially under the context of COVID-19 pandemic,is regarded as an emerging technology that can guarantee the adequate quality assurance and mitigate process risk while guaranteeing the desirable economic performance.Flexibility analysis is one approach to quantitively assess the capability of chemical process to guarantee feasible operation in face of variations on uncertain parameters.The aim of this paper is to provide the perspectives on the flexibility analysis for continuous pharmaceutical manufacturing processes.State-of-the-art and progress in the flexibility analysis for chemical processes including concept evolution,mathematical model formulations,solution strategies,and applications are systematically overviewed.Recent achievements on the flexibility/feasibility analysis of the downstream dosage form manufacturing process are also touched upon.Further challenges and developments in the field of flexibility analysis for novel continuous manufacturing processes of active pharmaceutical ingredients along with the integrated continuous manufacturing processes are identified.

Flexibility analysis for continuous ibuprofen manufacturing processes

Continuous ibuprofen(a widespread used analgesic drug)manufacturing is full of superiorities and is a fertile field both in industry and academia since it can not only effectively treat rheumatic and other chronic and painful diseases,but also shows great potential in dental diseases.As one of central elements of operability analysis,flexibility analysis is in charge of the quantitative assessment of the capability to guarantee the feasible operation in face of variations on uncertain parameters.In this paper,we focus on the flexibility index calculation for the continuous ibuprofen manufacturing process.We update existing state-of-the-art formulations,which traditionally lead to the max-max-max optimization problem,to approach the calculation of the flexibility index with a favorable manner.Advantages regarding the size of the mathematical model and the computational CPU time of the modified method are examined by four cases.In addition to identifying the flexibility index without any consideration of control variables,we also investigate the effects of different combinations of control variables on the flexibility property to reveal the benefits from taking recourse actions into account.Results from systematic investigations are expected to provide a solid basis for the further control system design and optimal operation of continuous ibuprofen manufacturing.

Data-driven optimal operation of the industrial methanol to olefin process based on relevance vector machine

Methanol to olefin(MTO)technology provides the opportunity to produce olefins from nonpetroleum sources such as coal,biomass and natural gas.More than 20 commercial MTO plants have been put into operation.Till now,contributions on optimal operation of industrial MTO plants from a process systems engineering perspective are rare.Based on relevance vector machine(RVM),a data-driven framework for optimal operation of the industrial MTO process is established to fully utilize the plentiful industrial data sets.RVM correlates the yield distribution prediction of main products and the operation conditions.These correlations then serve as the constraints for the multi-objective optimization model to pursue the optimal operation of the plant.Nondominated sorting genetic algorithmⅡis used to solve the optimization problem.Comprehensive tests demonstrate that the ethylene yield is effectively improved based on the proposed framework.Since RVM does provide the distribution prediction instead of point estimation,the established model is expected to provide guidance for actual production operations under uncertainty.

化学品智能制造的科学基础

基于第318期“双清论坛”,面向通过人工智能技术促进化工基础理论发展和指导产业化实践的目标,本文介绍了化学品智能制造的科学背景,凝练了人工智能方法与化工基础研究和产业化实践深度融合的关键科学问题,展示了近年来化学品制造工艺变革、技术和装备创新、系统集成和优化控制等方面的研究进展,对化工过程智能化研究提出建议,推动我国在化学品智能制造原理和方法上的重大创新。

Enterprise-wide optimization of integrated planning and scheduling for refinery-petrochemical complex with heuristic algorithm

This paper focuses on the integrated problem of long-term planning and short-term scheduling in a largescale refinery-petrochemical complex,and considers the overall manufacturing process from the upstream refinery to the downstream petrochemical site.Different time scales are incorporated from the planning and scheduling subproblems.At the end of each discrete time period,additional constraints are imposed to ensure material balance between different time scales.Discrete time representation is applied to the planning subproblem,while continuous time is applied to the scheduling of ethylene cracking and polymerization processes in the petrochemical site.An enterprise-wide mathematical model is formulated through mixed integer nonlinear programming.To solve the problem efficiently,a heuristic algorithm combined with a convolutional neural network(CNN),is proposed.Binary variables are used as the CNN input,leading to the integration of a data-driven approach and classical optimization by which a heuristic algorithm is established.The results do not only illustrate the detailed operations in a refinery and petrochemical complex under planning and scheduling,but also confirm the high efficiency of the proposed algorithm for solving large-scale problems.