Synthesis of indirect work exchange networks considering both isothermal and adiabatic process together with exergy analysis

In this paper, an efficient methodology for synthesizing the indirect work exchange networks (WEN) considering isothermal process and adiabatic process respectively based on transshipment model is first proposed. In contrast with superstructure method, the transshipment model is easier to obtain the minimum utility consumption taken as the objective function and more convenient for us to attain the optimal network configuration for further minimizing the number of units. Different from division of temperature intervals in heat exchange networks, different pressure intervals are gained according to the maximum compression/expansion ratio in consideration of operating principles of indirect work exchangers and the characteristics of no pressure constraints for stream matches. The presented approach for WEN synthesis is a linear programming model applied to the isothermal process, but for indirect work exchange networks with adiabatic process, a nonlinear programming model needs establishing. Additionally, temperatures should be regarded as decision variables limited to the range between inlet and outlet temperatures in each sub-network. The constructed transshipment model can be solved first to get the minimum utility consumption and further to determine the minimum number of units by merging the adjacent pressure intervals on the basis of the proposed merging methods, which is proved to be effective through exergy analysis at the level of units structures. Finally, two cases are calculated to confirm it is dramatically feasible and effective that the optimal WEN configuration can be gained by the proposed method.     

Prediction of maximum recoverable mechanical energy via work integration: A thermodynamic modeling and analysis approach

Thermal energy and mechanical energy are two common forms of energy consumed significantly in the process industries. While thermal energy can be effectively recovered using matured heat integration technologies, recovery of mechanical energy through work integration has not been fully explored. It is shown that work integration can be achieved through synthesizing work exchange networks (WENs), where work exchangers are operated in a batch mode, and compressors and expanders are operated in a continuous mode; this renders network synthesis a very sophisticated design task. It is greatly beneficial if the maximum amount of mechanical energy recoverable by a WEN can be determined prior to network design. In this article, we introduce a thermodynamic modeling and analysis method to identify accurately the maximum amount of recoverable mechanical energy of any process system of interest. The method is rigorous and general for target setting of mechanical energy recovery prior to WEN synthesis. © 2017 American Institute of Chemical Engineers AIChE J, 2017     

Step-wise synthesis of work exchange networks involving heat integration based on the transshipment model

Due to the deterioration of serious energy dilemma, energy-conservation and emission–reduction have been the strategic target in the past decades, thus people have identified the vital importance of higher energy efficiency and the influence of lower carbon development. Since work exchange network is a significant part of energy re-covery system, its optima design wil have dramatically significant effect on energy consumption reduction in chemical process system. With an extension of the developed transshipment model in isothermal process, a novel step-wise methodology for synthesis of direct work exchange network (WEN) in adiabatic process involv-ing heat integration is first proposed in this paper, where a nonlinear programming (NLP) model is formulated by regarding the minimum utility consumption as objective function and optimizing the initial WEN in accordance with the presented matching rules to get the optimized WEN configuration at first. Furthermore, we focus on the work exchange network synthesis with heat integration to attain the minimal total annual cost (TAC) with the introduction of heat-exchange equipment that is achieved by the following strategies in sequence:introducing heat-exchange equipment directly, adjusting the work quantity of the adjacent utility compressors or expanders, and approximating upper/lower pressure limits consequently to obtain considerable cost savings of expanders or compressors and work utility. Finally, a case taken from the literature is studied to illustrate the feasibility and effectiveness of the proposed method.     

Graph machine learning for design of high-octane fuels

Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO₂ emissions. Identification of molecules with desired autoignition properties indicated by a high research octane number and a high octane sensitivity is therefore of great practical relevance and can be supported by computer-aided molecular design (CAMD). Recent developments in the field of graph machine learning (graph-ML) provide novel, promising tools for CAMD. We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space. In particular, we explore the potential of Bayesian optimization and genetic algorithms in combination with generative graph-ML models. The graph-ML CAMD framework successfully identifies well-established high-octane components. It also suggests new candidates, one of which we experimentally investigate and use to illustrate the need for further autoignition training data.     

Simultaneous optimization of water and heat exchange networks

This paper focuses on the simultaneous optimization of the heat-integrated water allocation networks. A mathematic model is established to illustrate the modified state-space representation of this problem. An easy logical method is employed to help identify the streams of hot or cold ones. In this model, the water exchange networks (WEN), heat exchange networks (HEN), and the interactions between the WEN and HEN combine together as one unity. Thus, the whole network can be solved at one time, which enhances the possibility to get a global optimal result. Examples from the literature and a PVC plant are analyzed to illustrate the accuracy and applicability of this method.     

A multi-scale framework for CO2 capture,utilization, and sequestration: CCUS and CCU

We present a multi-scale framework for the optimal design of CO₂ capture, utilization, and sequestration (CCUS) supply chain network to minimize the cost while reducing stationary CO₂ emissions in the United States. We also design a novel CO₂ capture and utilization (CCU) network for economic benefit through utilizing CO₂ for enhanced oil recovery. Both the designs of CCUS and CCU supply chain networks are multi-scale problems which require decision making at material, process and supply chain levels. We present a hierarchical and multi-scale framework to design CCUS and CCU supply chain networks with minimum investment, operating and material costs. While doing so, we take into consideration the selection of source plants, capture processes, capture materials, CO₂ pipelines, locations of utilization and sequestration sites, and amounts of CO₂ storage. Each CO₂ capture process is optimized, and the best materials are screened from large pool of candidate materials. Our optimized CCUS supply chain network can reduce 50% of the total stationary CO₂ emission in the U.S. at a cost of $35.63 per ton of CO₂ captured and managed. The optimum CCU supply chain network can capture and utilize CO₂ to make a total profit of more than 555 million dollars per year ($9.23 per ton). We have also shown that more than 3% of the total stationary CO₂ emissions in the United States can be eliminated through CCU networks at zero net cost. These results highlight both the environmental and economic benefits which can be gained through CCUS and CCU networks. We have designed the CCUS and CCU networks through (i) selecting novel materials and optimized process configurations for CO₂ capture, (ii) simultaneous selection of materials and capture technologies, (iii) CO₂ capture from diverse emission sources, and (iv) CO₂ utilization for enhanced oil recovery. While we demonstrate the CCUS and CCU networks to reduce stationary CO₂ emissions and generate profits in the United States, the proposed framework can be applied to other countries and regions as well.     

Evaluating demand response opportunities for power systems resilience using MILP and MINLP Formulations

While peak shaving is commonly used to reduce power costs, chemical process facilities that can reduce power consumption on demand during emergencies (e.g., extreme weather events) bring additional value through improved resilience. For process facilities to effectively negotiate demand response (DR) contracts and make investment decisions regarding flexibility, they need to quantify their additional value to the grid. We present a grid-centric mixed-integer stochastic programming framework to determine the value of DR for improving grid resilience in place of capital investments that can be cost prohibitive for system operators. We formulate problems using both a linear approximation and a nonlinear alternating current power flow model. Our numerical results with both models demonstrate that DR can be used to reduce the capital investment necessary for resilience, increasing the value that chemical process facilities bring through DR. However, the linearized model often underestimates the amount of DR needed in our case studies. Published 2018. This article is a U.S. Government work and is in the public domain in the USA. AIChE J, 65: e16508, 2019     

Bioinspired multilevel interconnected networks with porous multiwalled nanotubes built by heterogeneous nanocrystallites

Inspired by fiber-interwoven eggshell membrane (ESM) with associated specific permeability and transport performance, interconnected nanotube networks are constructed to empolder relevant distinctive properties and functional advantages. In this work, a typical ESM-templated procedure combined with stepwise impregnation and gradient calcination is developed to accomplish the fabrication of multilevel frameworks from TiO₂ nanocrystallites to porous multiwalled nanotubes (NTs) to final interconnected networks. During the impregnation process, in situ mineralization occurs on ESM fiber substrate to generate Ti-impregnant coating with variant composition and layered structure. Followed by the calcination, anatase TiO₂ porous NTs come into being as ESM templates decompose at 500°C, then heterogeneous nanocrystallites are achieved at higher temperature. Thus, the final ESM-morphic nanocomposites present interconnected networks woven by porous multiwalled NTs. Benefiting from interconnected multichannels of multilevel networks and controllable heterogeneous nanocrystallites, the ESM-inspired TiO₂ would behave higher light-excitation and transport efficiency, which can achieve more valuable applications such as photocatalytic degradation to some organic pollutants.     

Lightweight and resilient SiOC/SiC foam with efficient electromagnetic wave absorption and high-temperature stability

Integrating multiple functions such as high electromagnetic (EM) wave absorption, thermal insulation, and resilience into one material is critical, especially for applications in harsh environment. SiC ceramic has received considerable attention as high-temperature wave absorber, but its applications are limited by common wave absorption performance and brittleness of ceramics. Here by incorporating SiO₂ with SiC in a unique three-dimensional network structure, SiOC/SiC foam consisting of abundant SiOC thin flakes interconnected by numerous long interweaving SiC nanowires have been prepared. The foam shows high EM wave absorption with minimum reflection loss of −30.23 dB, broad effective absorption bandwidth of 5.4 GHz, and a nearly complete compressive resilience from 10% strain. Besides, the foam displays high-temperature resistance up to 1400°C in air and good thermal insulation performance. Such multifunctional material is promising for applications in advanced aerospace industry under extreme conditions.     

Surplus diagram and cascade analysis technique for targeting property-based material reuse network

Recycle of process and waste streams are among the most effective resource conservation and waste reduction strategies. In many cases, recycle/reuse is dictated by sink constraints on properties of the recycled streams. In this work, we introduce an algebraic technique to establish rigorous targets on the minimum usage of fresh resources, maximum direct reuse, and minimum waste discharge for property-based material reuse network. Two new tools have been developed. A new graphical tool called the property surplus diagram is firstly introduced to provide a basic framework for determining rigorous targets for minimum fresh usage, maximum recycle, and minimum waste discharge. The tools also determine the property-based material recycle pinch location. The Property Cascade Analysis (PCA) technique is next established to set targets via a tabular approach. PCA eliminates the iterative steps typically associated with a graphical approach. Along with the minimum fresh and waste targets, the material allocation target is another key feature of the PCA. A network design technique is also presented in this paper to synthesise a maximum resource recovery (MRR) network that achieves the various established targets. The procedures developed in this paper constitute a generalisation to the composition-based graphical and algebraic techniques developed for water and hydrogen recovery networks. Two case studies are solved to illustrate the applicability of the developed procedures.     

An experimental verification of morphology of ibuprofen crystals from CAMD designed solvent

In our previous work [Karunanithi et al., 2006. A computer-aided molecular design framework for crystallization solvent design. Chemical Engineering Science 61, 1247-1260] we proposed a computer-aided molecular design (CAMD) framework to design solvents for crystallization processes. One of the important aspects of that work was the consideration of a qualitative property, namely crystal morphology, along with other physico-chemical properties (quantitative) of the solvents within the modeling framework. However, it is our view that consideration of any qualitative property, such as morphology of crystals formed from solvents, necessitates additional experimental verification steps. In this work we report the experimental verification of crystal morphology for the case study, solvent design for ibuprofen crystallization, presented in Karunanithi et al. [2006. A computer-aided molecular design framework for crystallization solvent design. Chemical Engineering Science 61, 1247-1260]. This we believe is an important step for the validation of the proposed solvent design model.     

Equation‐oriented optimization of process flowsheets with dividing‐wall columns

We present a new modeling approach for dividing‐wall columns (DWCs) that is amenable to equation‐oriented flowsheet simulation and optimization. The material, equilibrium, summation, and heat (MESH) equations describing a DWC are highly coupled and nonlinear, making DWC‐based process flowsheets challenging to simulate. Design optimization poses further challenges, typically requiring integer variables to select the number of column stages. To address these difficulties, we represent DWCs as networks of pseudo‐transient (differential‐algebraic) subunit models. We show that these networks have the same steady‐state solution as the original (algebraic) MESH equations, but present significant numerical benefits. We then embed these models in a previously developed pseudo‐transient flowsheet modeling and optimization framework. We further reformulate the models to require only continuous decision variables when selecting the optimal number of stages during design optimization. To illustrate these concepts, we discuss the DWC‐based intensification of the dimethyl ether process. © 2015 American Institute of Chemical Engineers AIChE J, 62: 704–716, 2016     

An efficient method for optimal design of large-scale integrated chemical production sites with endogenous uncertainty

Integrated sites are tightly interconnected networks of large-scale chemical processes. Given the large-scale network structure of these sites, disruptions in any of its nodes, or individual chemical processes, can propagate and disrupt the operation of the whole network. Random process failures that reduce or shut down production capacity are among the most common disruptions. The impact of such disruptive events can be mitigated by adding parallel units and/or intermediate storage. In this paper, we address the design of large-scale, integrated sites considering random process failures. In a previous work (Terrazas-Moreno et al., 2010), we proposed a novel mixed-integer linear programming (MILP) model to maximize the average production capacity of an integrated site while minimizing the required capital investment. The present work deals with the solution of large-scale problem instances for which a strategy is proposed that consists of two elements. On one hand, we use Benders decomposition to overcome the combinatorial complexity of the MILP model. On the other hand, we exploit discrete-rate simulation tools to obtain a relevant reduced sample of failure scenarios or states. We first illustrate this strategy in a small example. Next, we address an industrial case study where we use a detailed simulation model to assess the quality of the design obtained from the MILP model.     

水夹点分析与数学规划法相结合的用水网络优化设计

提出了水夹点分析和数学规划法相结合的用水网络最优设计法。水夹点分析基于对过程用水的理解,获得新鲜水用量目标并给出用水网络设计的基本规则。在此基础上建立过程使用新鲜水、排放废水和回用的各种可能匹配方案的用水网络超结构及其MINLP模型。既避免了用水夹点综合设计用水网络得不到真正意义上的最优解,又在一定程度上防止超结构规模过大,MINLP维数太高,求解困难。采用通用代数建模系统GAMS得到用水网络最优设计方案。文献中的应用实例表明,本文所提方法可充分发挥水夹点分析确定新鲜水用量或回用结构的简洁实用性和超结构MINLP寻求最佳方案的优点。     

Integrating expanders into heat exchanger networks above ambient temperature

The integration of expanders into heat exchanger networks (HENs) is a complex task since both heat and work are involved. In addition, the role of streams (as hot or cold), the utility demand, and the location of pinch points may change. With certain well‐defined conditions, four theorems are proposed for the integration of expanders into HENs above ambient temperature with the objective of minimizing exergy consumption. A straightforward graphical methodology for above ambient HENs design including expanders is developed on the basis of Grand Composite Curves (GCCs). It is concluded that to achieve a design with minimum exergy consumption, stream splitting may be applied and expansion should be done at pinch temperatures, hot utility temperature, or ambient temperature. In the majority of cases, however, and in line with the concept of Appropriate Placement from Pinch Analysis, expansion at pinch temperatures gives the minimum exergy consumption. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3404–3422, 2015     

Global optimization of reactive distillation networks using IDEAS

In this work, we present a methodology for the global optimization of reactive distillation (RD) networks, through the Infinite DimEnsionAl State-space (IDEAS) approach. Within the IDEAS framework, network synthesis is formulated as an infinite dimensional linear optimization problem. The IDEAS conceptual framework is realized through solution of a series of finite dimensional linear programs whose optimum values converge to the infinite program’s infimum. The proposed optimal design methodology is demonstrated on a case study involving reactive distillation-based synthesis of methyl tert-butyl ether (MTBE) from isobutene and methanol.     

Systematic methods and tools for design of sustainable chemical processes for CO2 utilization

A systematic computer-aided framework for sustainable process design is presented together with its application to the synthesis and generation of processing networks for dimethyl carbonate (DMC) production with CO₂ utilization. The framework integrated with various methods, tools, algorithms and databases is based on a combined process synthesis–design–intensification method. The method consists of three stages. The synthesis-stage involves superstructure based optimization to identify promising networks that convert a given set of raw materials to a desired set of products. The design-stage involves selection and analysis of the identified networks as a base case design in terms of operational feasibility, economics, life cycle assessment factors and sustainability measures, which are employed to establish targets for improvement in the next-stage. The innovation-stage involves generation and screening of the more sustainable alternatives through a phenomena-based process intensification method. Applications of the framework are highlighted for the DMC production process.     

Availability and reliability considerations in the design and optimisation of flexible utility systems

Industrial utility plants are usually comprised of many interconnected units that must constitute a flexible and reliable system capable of meeting process energy requirements under different circumstances (e.g. varying prices, demands, or equipment shutdowns). Also, in order to avoid large economic penalties, the design and operation of a utility plant should consider that the equipment is not fully reliable and that each item needs to receive preventive and corrective maintenance. Conventionally, these issues are handled by installing additional units according to rules of thumb or heuristics, which usually imply excessive capital costs and might even result in designs that cannot satisfy the specified demands for certain situations. In contrast, during the present work a systematic methodology has been developed to address the design and operation of flexible utility plants incorporating reliability and availability considerations. The suggested method is based on a novel modelling and optimisation framework that can address grassroots design, retrofit, or (pure) operation problems in which design and operational parameters are optimised simultaneously throughout several scenarios. Thereafter, it is possible to define maintenance and failure situations in different operating periods to ensure that the plant will be able to cope with them, while meeting process requirements at minimum cost. Hence, for design cases, the most cost-effective elements of redundancy can be determined without pre-specifying any structural options in the final configuration (e.g. equipment sizes, types, and number of units). Furthermore, the proposed (multiperiod) MILP formulation is robust enough to tackle problems of the size and complexity commonly found in industry, and has the potential of yielding significant economic savings.     

Quasi-decentralized model-based networked control of process systems

This paper develops a quasi-decentralized control framework for plants with distributed, interconnected units that exchange information over a shared communication network. In this architecture, each unit in the plant has a local control system that communicates with the plant supervisor – and with other local control systems – through a shared communication medium. The objective is to design an integrated control and communication strategy that ensures the desired closed-loop stability and performance for the plant while minimizing network utilization and communication costs. The idea is to reduce the exchange of information between the local control systems as much as possible without sacrificing stability of the individual units and the overall plant. To this end, dynamic models of the interconnected units are embedded in the local control system of each unit to provide it with an estimate of the evolution of its neighbors when measurements are not transmitted through the network. The use of a model to recreate the interactions of a given unit with one of its neighbors allows the sensor suite of the neighboring unit to send its data in a discrete fashion since the model can provide an approximation of the unit’s dynamics. The state of each model is then updated using the actual state of the corresponding unit provided by its sensors at discrete time instances to compensate for model uncertainty. By formulating the networked closed-loop plant as a hybrid system, an explicit characterization of the maximum allowable update period (i.e., minimum cross communication frequency) between each control system and the sensors of its neighboring units is obtained in terms of the degree of mismatch between the dynamics of the units and the models used to describe them. The developed control strategy is illustrated using a network of interconnected chemical reactors with recycle.