Supply chain risk identification using a HAZOP‐based approach

Risk management has become imperative for today's complex supply chains. Most approaches reported in the literature have been ad‐hoc and specific to certain risks; a general and comprehensive approach is lacking. To address this, we present a structured methodology for risk identification. Supply chain networks are in many ways similar to chemical plants, therefore well‐established methods and concepts from chemical process risk management can be adapted to supply chains. Drawing from this analogy, we propose to represent supply chain structure and operations using flow and work‐flow diagrams, equivalent to process flow diagrams (PFDs) and operating procedures. Following the HAZard and OPerability (HAZOP) analysis method common in process safety, risk identification can be performed by systematically generating deviations in different supply chain parameters and identifying their possible causes, consequences, safeguards, and mitigating actions. The application and benefits of the proposed approach are demonstrated using a refinery supply chain case study. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

State reduction in molecular simulations

Model reduction is an important systems task with a long history in traditional chemical engineering modeling. We discuss its interplay with modern data-mining tools (such as Local Feature Analysis and Diffusion Maps) through illustrative examples, and comment on important open issues regarding applications to large systems arising in molecular/atomistic simulations.     

Designing biomass supply chains within planetary boundaries

The design of sustainable supply chains, which recently emerged as an active area of research in process systems engineering, is vital to ensure sustainable development. Despite past and ongoing efforts, the available methods often overlook impacts beyond climate change or incorporate them via standard life cycle assessment metrics that are hard to interpret from an absolute sustainability viewpoint. We here address the design of biomass supply chains considering critical ecological limits of the Earth—planetary boundaries—which should never be surpassed by anthropogenic activities. Our method relies on a mixed-integer linear program that incorporates a planetary boundaries-based damage model to quantify absolute sustainability precisely. We apply this approach to the sugarcane-to-ethanol industry in Argentina, identifying the optimal combination of technologies and network layout that minimize the impact on these ecological boundaries. Our framework can find applications in a wide range of supply chain problems related to chemicals and fuels production, energy systems, and agriculture planning.     

Process systems engineering tools in the pharmaceutical industry

The purpose of this paper is to provide a summary of the current state of the application of process systems engineering tools in the pharmaceutical industry. In this paper, we present the compiled results of an industrial questionnaire submitted to pharmaceutical industry professionals. The topics covered in the questionnaire include process analytics, process monitoring, plant-wide information systems, unit operation modeling, quality control, and process optimization. A futuristic view of what process systems engineering tools will enable the pharmaceutical industry will be also be presented. While the industry is regularly using the traditional Design of Experiments approach to identify key parameters and to define control spaces, these approaches result in passive control strategies that do not attempt to compensate for disturbances. Special new approaches are needed for batch processes due to their essential dependence on time-varying conditions. Lastly, we briefly describe a novel data driven modeling approach, called Design of Dynamic Experiments that enables the optimization of batch processes with respect to time-varying conditions through an example of a simulated chemical reaction process. Many more approaches of this type are needed for the calculation of the design and control spaces of the process, and the effective design of feedback systems.     

A hybrid science-guided machine learning approach for modeling chemical processes: A review

This study presents a broad perspective of hybrid process modeling combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach. We divide the approach into two major categories: ML complements science, and science complements ML. We review the literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. For applying ML to improve science-based models, we present expositions of direct serial and parallel hybrid modeling and their combinations, inverse modeling, reduced-order modeling, quantifying uncertainty in the process and even discovering governing equations of the process model. For applying scientific principles to improve ML models, we discuss the science-guided design, learning and refinement. For each subcategory, we identify its requirements, strengths, and limitations, together with their published and potential applications. We also present several examples to illustrate different hybrid SGML methodologies for modeling chemical processes.     

Advanced trust region optimization strategies for glass box/black box models

We present an improved trust region filter (TRF) method for optimization of combined glass box/black box systems. Glass box systems refer to models that are easily expressed in an algebraic modeling language, providing cheap and accurate derivative information. By contrast, black box systems may be computationally expensive and derivatives are unavailable. The TRF method, as first introduced in our previous work (Eason and Biegler, AIChE J. 2016; 62:3124–3136), is able to handle hybrid systems containing both glass and black box components, which can frequently arise in chemical engineering, for example, when a multiphase reactor model is included in a flow sheet optimization problem. We discuss several recent modifications in the algorithm such as the sampling region, which maintains the algorithm's global convergence properties without requiring the trust region to shrink to zero in the limit. To benchmark the development of this optimization method, a test set of problems is generated based on modified problems from the CUTEr and COPS sets. The modified algorithm demonstrates improved performance using the test problem set. Finally, the algorithm is implemented within the Pyomo environment and demonstrated on a rigorous process optimization case study for carbon capture. © 2018 American Institute of Chemical Engineers AIChE J, 64: 3934–3943, 2018     

Among the trends for a modern chemical engineering,the third paradigm: The time and length multiscale approach as an efficient tool for process intensification and product design and engineering

To respond to the changing needs of the chemical and related industries in order both to meet today's economy demands and to remain competitive in global trade, a modern chemical engineering is vital to satisfy both the market requirements for specific nano and microscale end-use properties of products, and the social and environmental constraints of industrial meso and macroscale processes. Thus an integrated system approach of complex multidisciplinary, non-linear, non-equilibrium processes and phenomena occurring on different length and time scales of the supply chain is required. That is, a good understanding of how phenomena at a smaller length-scale relates to properties and behaviour at a longer length-scale is necessary (from the molecular-scale to the production-scales). This has been defined as the triplet “molecular Processes-Product-Process (3PE)” integrated multiscale approach of chemical engineering. Indeed a modern chemical engineering can be summarized by four main objectives: (1) Increase productivity and selectivity through intensification of intelligent operations and a multiscale approach to processes control: nano and micro-tailoring of materials with controlled structure. (2) Design novel equipment based on scientific principles and new production methods: process intensification using multifunctional reactors and micro-engineering for micro structured equipment. (3) Manufacturing end-use properties to synthesize structured products, combining several functions required by the customer with a special emphasis on complex fluids and solid technology, necessating molecular modeling, polymorph prediction and sensor development. (4) Implement multiscale application of computational chemical engineering modeling and simulation to real-life situations from the molecular-scale to the production-scale, e.g., in order to understand how phenomena at a smaller length-scale relate to properties and behaviour at a longer length-scale. The presentation will emphasize the 3PE multiscale approach of chemical engineering for investigations in the previous objectives and on its success due to the today's considerable progress in the use of scientific instrumentation, in modeling, simulation and computer-aided tools, and in the systematic design methods.     

基于被控变量在线建模的化工过程实时优化方法

选择合适的被控变量可对过程进行实时优化(RTO),但现有方法在设计阶段确定被控变量后,不允许对其进行在线调整,导致了RTO效果的局限性。针对这一问题,提出了一种基于被控变量在线建模的方法,使用局部建模技术在线寻找相似样本并建立一阶最优性必要条件(NCO)的估计模型,将其作为被控变量更新控制回路,在反馈控制作用下达到更好的RTO效果。对一个蒸发过程的研究表明,此方法能够通过对NCO的在线准确建模,增加生产过程的经济效益。     

Robust decision making for hybrid process supply chain systems via model predictive control

Model predictive control (MPC) is a promising solution for the effective control of process supply chains. This paper presents an optimization-based decision support tool for supply chain management, by means of a robust MPC strategy. The proposed formulation: (i) captures uncertainty in model parameters and demand by stochastic programming, (ii) accommodates hybrid process systems with decisions governed by logical conditions/rulesets, and (iii) addresses multiple supply chain performance metrics including customer service and economics, within an integrated optimization framework. Two mechanisms for uncertainty propagation are presented – an open-loop approach, and an approximate closed-loop strategy. The performance of the robust MPC framework is analyzed through its application to two process supply chain case studies. The proposed approach is shown to provide a substantial reduction in the occurrence of back orders when compared to a nominal MPC implementation.     

化学工业"两化融合"发展与过程系统工程:挑战和前景(二)——"两化融合"发展对过程系统工程的挑战及前景

论述了化学工业"两化融合"发展,趋向精细化、服务化和可持续化,对过程系统工程(PSE)提出挑战.研究了过程系统工程应从产品工程/纳米过程系统工程、间歇过程系统工程、供应链的优化与协同、多尺度过程集成及绿色过程系统工程5个方面提供技术支撑的前景.     

Explicit model predictive control through robust optimization

A strategy that calculates an explicit state feedback policy to regulate constrained uncertain discrete-time uncertain linear systems is presented. We consider uncertain processes, affected by box-bounded multiplicative uncertainty as well as bounded additive uncertainty with linear state and inputs constraints. The proposed method includes (i) the calculation of a terminal set constraint and (ii) the robust reformulation of state constraints in the prediction horizon. These features allow the derivation of the desired policy by solving a single multiparametric quadratic programming problem that guarantees feasible operation in the presence of uncertainty. Additionally, we employ variable and constraint elimination approaches to enhance the computational performance of the strategy. We demonstrate the steps and benefits of these developments with a numerical example and a chemical engineering case study.     

A control-relevant approach to demand modeling for supply chain management

The development of control-oriented decision policies for inventory management in supply chains has drawn considerable interest in recent years. Modeling demand to supply forecasts is an important component of an effective solution to this problem. Drawing from the problem of control-relevant parameter estimation, this paper presents an approach for demand modeling in a production-inventory system that relies on a specialized weight to tailor the emphasis of the fit to the intended purpose of the model, which is to provide forecasts to inventory management policies based on internal model control or model predictive control. A systematic approach to generate this weight function (implemented using data prefilters in the time domain) is presented and the benefits demonstrated on a series of representative case studies. The multi-objective formulation developed in this work allows the user to emphasize minimizing inventory variance, minimizing starts variance, or their combination, as dictated by operational and enterprise goals.     

Biomass-to-bioenergy and biofuel supply chain optimization: Overview,key issues and challenges

This article describes the key challenges and opportunities in modeling and optimization of biomass-to-bioenergy supply chains. It reviews the major energy pathways from terrestrial and aquatic biomass to bioenergy/biofuel products as well as power and heat with an emphasis on “drop-in” liquid hydrocarbon fuels. Key components of the bioenergy supply chains are then presented, along with a comprehensive overview and classification of the existing contributions on biofuel/bioenergy supply chain optimization. This paper identifies fertile avenues for future research that focuses on multi-scale modeling and optimization, which allows the integration across spatial scales from unit operations to biorefinery processes and to biofuel value chains, as well as across temporal scales from operational level to strategic level. Perspectives on future biofuel supply chains that integrate with petroleum refinery supply chains and/or carbon capture and sequestration systems are presented. Issues on modeling of sustainability and the treatment of uncertainties in bioenergy supply chain optimization are also discussed.     

On the use of Principal Component Analysis for reducing the number of environmental objectives in multi-objective optimization: Application to the design of chemical supply chains

Multi-objective optimization (MOO) has recently attracted an increasing interest in environmental engineering. One major limitation of the existing solution methods for MOO is that their computational burden tends to grow rapidly in size with the number of environmental objectives. In this paper, we study the use of Principal Component Analysis (PCA) to identify redundant environmental metrics in MOO that can be omitted without disturbing the main features of the problem, thereby reducing the associated complexity. We show that, besides its numerical usefulness, the use of PCA coupled with MOO provides valuable insights on the relationships between environmental indicators of concern for decision-makers. The capabilities of the proposed approach are illustrated through its application to the design of environmentally conscious chemical supply chains (SCs).     

Semi-Markovian models of the functioning of complex chemical engineering systems

This article deals with the general approaches to the construction of semi-Markovian models of chemical engineering systems. The basic lines of development of the semi-Markovian modeling apparatus, used for the determination of the rational set of measures to ensure the models’ economically efficient functioning, are shown. Expressions for the characterization of the maintenance service system and the repair of complex chemical engineering facilities are suggested.     

Agent-based supply chain management—1: framework

In the face of highly competitive markets and constant pressure to reduce lead times, enterprises today consider supply chain management to be the key area where improvements can significantly impact the bottom line. More enterprises now consider the entire supply chain structure while taking business decisions. They try to identify and manage all critical relationships both upstream and downstream in their supply chains. Some impediments to this are that the necessary information usually resides across a multitude of resources, is ever changing, and is present in multiple formats. Most supply chain decision support systems (DSSs) are specific to an enterprise and its supply chain, and cannot be easily modified to assist other similar enterprises and industries. In this two-part paper, we propose a unified framework for modeling, monitoring and management of supply chains. The first part of the paper describes the framework while the second part illustrates its application to a refinery supply chain. The framework integrates the various elements of the supply chain such as enterprises, their production processes, the associated business data and knowledge and represents them in a unified, intelligent and object-oriented fashion. Supply chain elements are classified as entities, flows and relationships. Software agents are used to emulate the entities i.e. various enterprises and their internal departments. Flows—material and information—are modeled as objects. The framework helps to analyze the business policies with respect to different situations arising in the supply chain. We illustrate the framework by means of two case studies. A DSS for petrochemical cluster management is described together with a prototype DSS for crude procurement in a refinery.     

Financial and financial engineering considerations in supply chain and product development pipeline management

Enterprise-wide decision problems are receiving increasing attention in the process systems engineering literature. In particular, the supply chain and product development pipeline management components of this general class of problems have been subjects of intensive research in both their deterministic and their stochastic forms. The supply chain management (SCM) problem has seen work largely focused on the process operations and logistics components while for the product development pipeline management (PDPM) problem much of the attention has been on MILP formulations addressing the consequences of product failure during its development. In their full realization, both are recognized as challenging stochastic multi-stage decision problems. In this paper we discuss three important aspects of these problems that require further research: the realistic representation of the financial components and appropriate criteria for this class of problems, strategic management of supplier and customer relationships through inventory management and option contracts, and innovative approaches to suitably value and integrate a broader range of decisions available to management. We highlight and extend relevant contributions and case examples drawn from the recent literature that are emerging on these topics and use this work to point out further challenges.     

Divide and conquer strategies in large scale dynamic process simulation

The homogeneous modeling of dynamic processes in chemical plants by large systems of differential-algebraic equations and their parallelized numerical solution on multiprocessor systems has been proven to be an effective way to handle large-scale process models efficiently but requires a substantial effort in the case of model extensions. In contrast to it the heterogeneous modeling approach distributes the solution of submodels of interconnected processes to a computer network. The objective is the independent treatment of each submodel within a time interval with its most appropriate numerical method and the subsequent iterative matching of the interconnecting variables of the over-all process flowsheet. This strategy of distributed simulation is investigated in the case that the submodels do not provide input-output sensitivities for solving the connecting equations between submodels.