Computer Modeling System of the Industrial Diesel Fuel Catalytic Dewaxing Process

An unsteady mathematical model and a computer modeling system of the diesel fuel catalytic dewaxing process (mild hydrocracking) were developed. The modeling system allows for calculating the optimal technological mode to produce low‐freezing diesel fuel with the required cold filter plugging point taking into account the feedstock composition and catalyst activity. The modeling system consists of the main blocks: database, knowledge base, unsteady mathematical model of the diesel fuel catalytic dewaxing process, and application program package. Using the developed computer modeling system, the influence of the feedstock composition and flow rate as well as of the catalyst activity on the cold filter plugging point and the yield of diesel fuel is demonstrated.     

Optimization of IGCC processes with reduced order CFD models

Integrated gasification combined cycle (IGCC) plants have significant advantages for efficient power generation with carbon capture. Moreover, with the development of accurate CFD models for gasification and combined cycle combustion, key units of these processes can now be modeled more accurately. However, the integration of CFD models within steady-state process simulators, and subsequent optimization of the integrated system, still presents significant challenges. This study describes the development and demonstration of a reduced order modeling (ROM) framework for these tasks. The approach builds on the concepts of co-simulation and ROM development for process units described in earlier studies. Here we show how the ROMs derived from both gasification and combustion units can be integrated within an equation-oriented simulation environment for the overall optimization of an IGCC process. In addition to a systematic approach to ROM development, the approach includes validation tasks for the CFD model as well as closed-loop tests for the integrated flowsheet. This approach allows the application of equation-based nonlinear programming algorithms and leads to fast optimization of CFD-based process flowsheets. The approach is illustrated on two flowsheets based on IGCC technology.     

Optimal operation policies in batch reactors

Optimal operation policies in batch reactors are obtained using dynamic optimisation technique. Two different types of optimisation problems, namely, maximum conversion and minimum time problems are formulated and solved and optimal operation policies in terms of reactor temperature or coolant flow rate are obtained. A path constraint on the reactor temperature is imposed for safe reactor operation and an endpoint constraint on undesired waste production (by-product) is imposed to minimise environmental impact.Two different types of models are considered within the optimisation framework. The shortcut model allows determination of optimal reactor temperature profile to be used for detailed design of the reactor. The detailed model allows optimising operating conditions for an already designed batch reactors.     

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.     

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.     

Operation planning of energy storage system considering multiperiod energy supplies and demands

The energy storage system (ESS) is recently drawing an increasing attention as an efficient and affordable tool in energy systems. While most interest has been concerned with developing better ESS technology, attention should be given to constructing a rigorous decision-supporting framework for operating the ESS. Motivated by this need, a mathematical modeling framework for the operation of an ESS is proposed in this paper. The proposed framework allows us to prepare ESS operations such as when to charge or discharge by which amount over multiple time periods. Numerical examples are presented to illustrate the applicability of the proposed framework.     

A mass and energy balance stage model for cyclic distillation

Cyclic distillation is an emerging process intensification technology, which can improve separation efficiency compared to conventional distillation. As most current models only account for the mass transfer, there is a lack of a stage model for cyclic distillation processes, which includes considerations of both mass and energy transfer. Such a model is presented in this article, and using this model, selected case studies, describing binary and multiple component systems with both ideal and nonideal liquid phases, are investigated. The presented stage model allows for the modeling of both mass and energy transfer for a cyclic distillation process and allows for multiple feed locations, as well as side draws. With the energy balances included, the dynamic vapor flow rate can be described. This was shown to have a significant effect on the separation, especially for cases where the change in the vapor flow over the column height was high.     

An optimization framework for the integration of water management and shale gas supply chain design

This study presents the mathematical formulation and implementation of a comprehensive optimization framework for the assessment of shale gas resources. The framework simultaneously integrates water management and the design and planning of the shale gas supply chain, from the shale formation to final product demand centers and from fresh water supply for hydraulic fracturing to water injection and/or disposal. The framework also addresses some issues regarding wastewater quality, i.e., total dissolved solids (TDS) concentration, as well as spatial and temporal variations in gas composition, features that typically arise in exploiting shale formations. In addition, the proposed framework also considers the integration of different modeling, simulation and optimization tools that are commonly used in the energy sector to evaluate the technical and economic viability of new energy sources. Finally, the capabilities of the proposed framework are illustrated through two case studies (A and B) involving 5 well-pads operating with constant and variable gas composition, respectively. The effects of the modeling of variable TDS concentration in the produced wastewater is also addressed in case study B.     

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.     

Dual mechanism model for fluid particle breakup in the entire turbulent spectrum

This work provides an in-depth understanding of different breakup mechanisms for fluid particles in turbulent flows. All the disruptive and cohesive stresses are considered for the entire turbulent energy spectrum and their contributions to the breakup are evaluated. A new modeling framework is presented that bridges across turbulent subranges. The model entails different mechanisms for breakup by abandoning the classical limitation of inertial models. The predictions are validated with experiments encompassing both breakup regimes for droplets stabilized by internal viscosity and interfacial tension down to the micrometer length scale, which covers both the inertial and dissipation subranges. The model performance ensures the reliability of the framework, which involves different mechanisms. It retains the breakup rate for inertial models, improves the predictions for the transition region from inertia to dissipation, and bridges seamlessly to Kolmogorov-sized droplets.     

Odd random phase multisine EIS for organic coating analysis

In this paper the different steps involved in an integrated methodology to measure, analyze and model electrochemical systems in a correct and reliable way are shown for Al-rich metallic coated steel with an additional organic coating. This methodology is based on the use of an odd random phase multisine excitation signal and was proposed by our group previously. The use of this multisine excitation signal decreases the measurement time. Moreover, it allows to perform a rigourous data-analysis and to quantify the signal-to-noise ratio, the level of non-linear and non-stationary behaviour. It was shown that by using this technique the optimal measurement conditions can be chosen based on a trade-off between signal-to-noise ratio and non-linear behaviour. Moreover, in the modeling procedure the noise, non-linearities or non-stationarities can be taken into account. The methodology allows a statistical evaluation of the proposed model for the coated metal, indicating whether model errors are still present or not.     

化工过程的智能混合建模方法及应用

随着人工智能技术和配套数据系统的快速发展,化工过程建模技术达到了新的高度,将多个机理模型和数据驱动模型以合理的结构加以组合的智能混合建模方法,可以综合利用化工过程的第一性原理及过程数据,结合人工智能算法以串联、并联或者混联的形式解决化工过程中的模拟、监测、优化和预测等问题,建模目的明确,过程灵活,形成的混合模型有着更好的整体性能,是近年来过程建模技术的重要发展趋势。本文围绕近年来针对化工过程的智能混合建模工作进行了总结,包括应用的机器学习算法、混合结构设计、结构选择等关键问题,重点论述了混合模型在不同任务场景下的应用。指出混合建模的关键在于问题和模型结构的匹配,而提高机理子模型性能,获取高质量宽范围的数据,深化对过程机理的理解,形成更有效率的混合建模范式,这些都是现阶段提高混合建模性能的研究方向。     

Development of a novel framework for the design of transport policies to achieve environmental targets

The formulation of policies requires the selection and configuration of effective and acceptable courses of action to reach explicit goals. A one-size-fits-all policy is unlikely to achieve the desired goals; as a result, the identification of a suite of alternative policies, together with clear indications of their trade-offs, is crucial to accommodate the diversity of stakeholders’ preferences. At present, the formulation of transport policies is done manually; this fact, together with the size of the space of possible policies, results in a large part of that space being left unexplored. A six-step framework to explore the space of alternative transport policies in order to achieve environmental targets is proposed. The process starts with a user-defined set of specific policy measures, using them as building blocks in the generation of alternative policy packages, clusters and future images according to the user's preferences and goals.The analysis framework is based on the visioning and backcasting approach used in the VIBAT report [Banister, D., & Hickman, R. (2006a). Visioning and backcasting for UK transport policy (VIBAT) project. Department for Transport's Horizons Research Programme 2004/06. The Bartlett school of planning and Halcrow Group Ltd. Retrieved 1/18/2008 http://www.ucl.ac.uk/ucft696/vibat2.html]. The framework is being implemented as a prototype decision support system around a case study: the formulation and analysis of policies required to achieve CO₂ emission targets for the transport sector in the UK. Important insights on how to develop the framework have also been elicited from engineering design. The goal is to accelerate the task of policy-making and improve the effectiveness of the resulting policies.The proposed method and computer implementation is fundamentally different from the tools commonly used in the transport sector and is intended to assist (not replace) transport policy makers, and complement (not substitute nor compete with) existing mathematical modelling tools. This research constitutes the first step towards the development of a general family of computer-based systems that support the design of policies to achieve environmental targets—not only for transport, but also for other sectors such as energy and water.     

Integrated process design,scheduling, and model predictive control of batch processes with closed-loop implementation

Simultaneous evaluation of multiple time scale decisions has been regarded as a promising avenue to increase the process efficiency and profitability through leveraging their synergistic interactions. Feasibility of such an integral approach is essential to establish a guarantee for operability of the derived decisions. In this study, we present a modeling methodology to integrate process design, scheduling, and advanced control decisions with a single mixed-integer dynamic optimization (MIDO) formulation while providing certificates of operability for the closed-loop implementation. We use multi-parametric programming to derive explicit expressions for the model predictive control strategy, which is embedded into the MIDO using the base-2 numeral system that enhances the computational tractability of the integrated problem by exponentially reducing the required number of binary variables. Moreover, we apply the State Equipment Network representation within the MIDO to systematically evaluate the scheduling decisions. The proposed framework is illustrated with two batch processes with different complexities.     

Unification of neural and statistical modeling methods that combine inputs by linear projection

Empirical modeling methods that combine inputs by linear projection include linear methods such as, ordinary least-squares regression, partial least-squares regression, principal components regression, and nonlinear methods such as, backpropagation networks with a single hidden layer, projection pursuit regression, nonlinear partial least-squares regression, and nonlinear principal components regression. In this paper, these popular modeling techniques are unified to yield a single method called nonlinear continuum regression (NLCR). This unification is based on the insight provided by a common framework for empirical modeling methods, and is achieved by using activation functions that adapt to the measured data, a common optimization criterion for finding the projection directions, and a hierarchical training methodology that allows efficient modeling. The adaptive-shape activation functions are determined by univariate smoothing in the space of the projected input versus output. The NLCR optimization criterion contains an adjustable parameter that controls the degree of overfitting or bias of the model, and spans the continuum of methods from projection pursuit regression or backpropagation networks to nonlinear principal components regression. Consequently, NLCR results in models that are usually more general and compact than those obtained by existing methods based on linear projection, while eliminating the need for arbitrary selection of an empirical modeling method based on linear projection for a given task. The improved modeling ability of NLCR and its performance on different types of training data are illustrated by examples based on simulated and industrial data.     

An integrated qualitative and quantitative modeling framework for computer‐assisted HAZOP studies

The article proposes a novel practical framework for computer‐assisted hazard and operability (HAZOP) that integrates qualitative reasoning about system function with quantitative dynamic simulation in order to facilitate detailed specific HAZOP analysis. The practical framework is demonstrated and validated on a case study concerning a three‐phase separation process. The multilevel flow modeling (MFM) methodology is used to represent the plant goals and functions. First, means‐end analysis is used to identify and formulate the intention of the process design in terms of components, functions, objectives, and goals on different abstraction levels. Based on this abstraction, qualitative functional models are constructed for the process. Next MFM‐specified causal rules are extended with systems specific features to enable proper reasoning. Finally, systematic HAZOP analysis is performed to identify safety critical operations, its causes and consequences. The outcome is a qualitative hazard analysis of selected process deviations from normal operations and their consequences as input to a traditional HAZOP table. The list of unacceptable high risk deviations identified by the qualitative HAZOP analysis is used as input for rigorous analysis and evaluation by the quantitative analysis part of the framework. To this end, dynamic first‐principles modeling is used to simulate the system behavior and thereby complement the results of the qualitative analysis part. The practical framework for computer‐assisted HAZOP studies introduced in this article allows the HAZOP team to devote more attention to high consequence hazards. © 2014 American Institute of Chemical Engineers AIChE J 60: 4150–4173, 2014