Optimal supply chain design and operations under multi‐scale uncertainties: Nested stochastic robust optimization modeling framework and solution algorithm

Although strategic and operational uncertainties differ in their significance of impact, a “one‐size‐fits‐all” approach has been typically used to tackle all types of uncertainty in the optimal design and operations of supply chains. In this work, we propose a stochastic robust optimization model that handles multi‐scale uncertainties in a holistic framework, aiming to optimize the expected economic performance while ensuring the robustness of operations. Stochastic programming and robust optimization approaches are integrated in a nested manner to reflect the decision maker's different levels of conservativeness toward strategic and operational uncertainties. The resulting multi‐level mixed‐integer linear programming model is solved by a decomposition‐based column‐and‐constraint generation algorithm. To illustrate the application, a county‐level case study on optimal design and operations of a spatially‐explicit biofuel supply chain in Illinois is presented, which demonstrates the advantages and flexibility of the proposed modeling framework and efficiency of the solution algorithm. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3041–3055, 2016     

Multi‐Objective Optimization of Pseudo‐Dynamic Operation of Naphtha Pyrolysis by a Surrogate Model

A simple pseudo‐dynamic surrogate model is developed in the framework of the state space model with the feed‐forward neural network to replace the complex free radical pyrolysis model. The surrogate model is then applied to investigate the multi‐objective optimization of two key performance objectives with distinct contradiction: the mean yields of key products and the day mean profits. The ?‐constraint method is employed to solve the multi‐objective optimization problem, which provides a broad range of operation conditions depicting tradeoffs of both key objectives. The Pareto‐optimal frontier is successfully obtained and five selected cases on the frontier are discussed, suggesting that flexible operations can be performed based on industrial demands.     

Multi‐period planning,design, and strategic models for long‐term,quality‐sensitive shale gas development

In this work we address the long‐term, quality‐sensitive shale gas development problem. This problem involves planning, design, and strategic decisions such as where, when, and how many shale gas wells to drill, where to lay out gathering pipelines, as well as which delivery agreements to arrange. Our objective is to use computational models to identify the most profitable shale gas development strategies. For this purpose we propose a large‐scale, nonconvex, mixed‐integer nonlinear programming model. We rely on generalized disjunctive programming to systematically derive the building blocks of this model. Based on a tailor‐designed solution strategy we identify near‐global solutions to the resulting large‐scale problems. Finally, we apply the proposed modeling framework to two case studies based on real data to quantify the value of optimization models for shale gas development. Our results suggest that the proposed models can increase upstream operators’ profitability by several million U.S. dollars. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2296–2323, 2016     

CO2 methanation: Optimal start‐up control of a fixed‐bed reactor for power‐to‐gas applications

Utilizing volatile renewable energy sources (e.g., solar, wind) for chemical production systems requires a deeper understanding of their dynamic operation modes. Taking the example of a methanation reactor in the context of power‐to‐gas applications, a dynamic optimization approach is used to identify control trajectories for a time optimal reactor start‐up avoiding distinct hot spot formation. For the optimization, we develop a dynamic, two‐dimensional model of a fixed‐bed tube reactor for carbon dioxide methanation which is based on the reaction scheme of the underlying exothermic Sabatier reaction mechanism. While controlling dynamic hot spot formation inside the catalyst bed, we prove the applicability of our methodology and investigate the feasibility of dynamic carbon dioxide methanation. © 2016 American Institute of Chemical Engineers AIChE J, 63: 23–31, 2017     

Toward more cost‐effective and greener chemicals production from shale gas by integrating with bioethanol dehydration: Novel process design and simulation‐based optimization

A novel process design for a more cost‐effective, greener process for making chemicals from shale gas and bioethanol is presented. The oxidative coupling of methane and cocracking technologies are considered for converting methane and light natural gas liquids, into value‐added chemicals. Overall, the process includes four process areas: gas treatment, gas to chemicals, methane‐to‐ethylene, and bioethanol‐to‐ethylene. A simulation‐optimization method based on the NSGA‐II algorithm for the life cycle optimization of the process modeled in the Aspen HYSYS is developed. An energy integration model is also fluidly nested using the mixed‐integer linear programming. The results show that for a “good choice” optimal design, the minimum ethylene selling price is $655.1/ton and the unit global‐warming potential of ethylene is 0.030 kg CO₂‐eq/kg in the low carbon shale gas scenario, and $877.2/ton and 0.360 kg CO₂‐eq/kg in the high carbon shale gas scenario. © 2014 American Institute of Chemical Engineers AIChE J, 61: 1209–1232, 2015     

Dynamic real‐time optimization with closed‐loop prediction

Process plants are operating in an increasingly global and dynamic environment, motivating the development of dynamic real‐time optimization (DRTO) systems to account for transient behavior in the determination of economically optimal operating policies. This article considers optimization of closed‐loop response dynamics at the DRTO level in a two‐layer architecture, with constrained model predictive control (MPC) applied at the regulatory control level. A simultaneous solution approach is applied to the multilevel DRTO optimization problem, in which the convex MPC optimization subproblems are replaced by their necessary and sufficient Karush–Kuhn–Tucker optimality conditions, resulting in a single‐level mathematical program with complementarity constraints. The performance of the closed‐loop DRTO strategy is compared to that of the open‐loop prediction counterpart through a multi‐part case study that considers linear dynamic systems with different characteristics. The performance of the proposed strategy is further demonstrated through application to a nonlinear polymerization reactor grade transition problem. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3896–3911, 2017     

Simulation and application of response surface methodology to a nylon‐6 hydrolytic polymerization in a semibatch reactor

This work presents an experimental design methodology combined with computational simulation to correlate the influence of operational conditions and reactants charge in the numeric average molecular weight (MWN) as well as on monomer conversion (X_CL), for the hydrolytic polymerization of nylon‐6 in a semibatch reactor. It evaluated the reaction temperature, the pressure profile, and the proportion of reactants in the charge. Experimental design was used to screen the most statistically significant variables and to develop a reliable predictive model for each response. The combined use of the models can be applied for process optimization, by establishing MWN and maximum X_CL as objective functions. Responses surface allowed the visualization of the responses behavior when changing the independent variables and therefore to identify the optimal tendencies. This work demonstrates that such methodology can be applied for optimization of complex processes like the hydrolytic polymerization of nylon‐6. This polymerization has many side reactions occurring at the same time, which are sensitive to different profiles of pressure and temperature that are applied. This evaluation is quite interesting as such profiles are necessary to perform the several polymerization steps and have a significant impact on product characteristics and therefore in its applications. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

Computer‐aided design of tailor‐made ionic liquids

Ionic liquids (IL), with their negligible vapor pressure, have the potential to replace volatile organic solvents in several processes. They also exhibit other unique characteristics, such as high thermal stability, wide liquid range, and wide electrochemical window, which make them attractive for many important applications. In addition, millions of ILs can be formed through different combination of cations, anions, and other functional groups. Till now, majority of work on IL selection, for a given application, is guided by trial and error experimentation. In this article, we present a computer‐aided IL design framework, based on semiempirical structure‐property models and optimization methods, which can consider several IL candidates and design optimal structures for a given application. This powerful methodology has great potential to act as a knowledge‐based framework to aid synthetic chemists and engineers develop new ILs. © 2013 American Institute of Chemical Engineers AIChE J, 59: 4627–4640, 2013     

Process design and control optimization: A simultaneous approach by multi‐parametric programming

We present a framework for the application of design and control optimization via multi‐parametric programming through four case studies. We develop design dependent multi‐parametric model predictive controllers that are able to provide the optimal control actions as functions of the system state and the design of the process at hand, via our recently introduced PAROC framework (Pistikopoulos et al, Chem Eng Sci. 2015;136:115–138). The process and the design dependent explicit controllers undergo a mixed integer dynamic optimization (MIDO) step for the determination of the optimal design. The result of the MIDO is the optimal design of the process under optimal operation. We demonstrate the framework through case studies of a tank, a continuously stirred tank reactor, a binary distillation column and a residential cogeneration unit. © 2017 American Institute of Chemical Engineers AIChE J, 2017     

Numerical modelling and simulation of pulverized solid‐fuel combustion in swirl burners

A finite‐volume numerical model for computer simulation of pulverized solid‐fuel combustion in furnaces with axisymmetric‐geometry swirl burner is presented. The simulation model is based on the k ? ε single phase turbulence model, considering the presence of the dispersed solid phase via additional source terms in the gas phase equations. The dispersed phase is treated by the particle source in cell (PSIC) method. Solid fuel particle devolatilization, homogenous and heterogeneous chemical reaction processes are modelled via a global combustion model. The radiative heat transfer equation is also resolved using the finite volume method. The numerical simulation code is validated by comparing computational and experimental results of pulverized coal in an experimental furnace equipped with a swirl burner. It is shown that the developed numerical code can successfully predict the flow field and flame structure including swirl effects and can therefore be used for the design and optimization of pulverized solid‐fuel swirl burners.     

Worst‐case design of subsea production facilities using semi‐infinite programming

The problem of designing novel process systems for deployment in extreme and hostile environments is addressed. Specifically, the process system of interest is a subsea production facility for ultra deepwater oil and gas production. The costs associated with operational failures in deepwater environments are prohibitively high and, therefore, warrant the application of worst‐case design strategies. That is, prior to the construction and deployment of a process, a certificate of robust feasibility is obtained for the proposed design. The concept of worst‐case design is addressed by formulating the design feasibility problem as a semi‐infinite optimization problem with implicit functions embedded. A basic model of a subsea production facility is presented for a case study of rigorous performance and safety verification. Relying on recent advances in global optimization of implicit functions and semi‐infinite programming, the design feasibility problem is solved, demonstrating that this approach is effective in addressing the problem of worst‐case design of novel process systems. © 2014 American Institute of Chemical Engineers AIChE J, 60: 2513–2524, 2014     

Synthesis of C‐H‐O Symbiosis Networks

The concept of synthesizing carbon, hydrogen, and oxygen (C‐H‐O) SYmbiosis Networks (CHOSYNs) for the design of eco‐industrial parks is introduced. Within a CHOSYN, compounds containing C‐H‐O are exchanged, converted, separated, mixed, and allocated. The use of C‐H‐O as the basis for integration creates numerous opportunities for synergism because C, H, and O are the primary building blocks for many industrial compounds that can be exchanged and integrated. A particularly attractive feature of the CHOSYN framework is its ability to use atomic‐based targets to establish benchmarks for the design of macroscopic systems involving multiple processes. Several structural representations, benchmarking, and optimization formulations are developed to embed potential CHOSYN configurations of interest and to synthesize cost‐effective networks. A case study with several scenarios is solved to demonstrate the new concept and tools. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1242–1262, 2015     

Optimal Design and Economic Evaluation of Dividing‐Wall Columns

In an effort to reduce costs, a systematic optimization approach is proposed to address the energy consumption of dividing‐wall columns (DWCs). This iterative optimization procedure begins by minimizing the overall heat duty using an innovative objective function within a constrained design space. A sensitivity analysis is then carried out on the manipulated variables to obtain their optimal ranges. Optimal operating parameters are obtained through the evaluation of the total annualized cost (TAC). For the separation process of benzene/toluene/o‐xylene, the optimal DWC flow sheet exhibits a significant decrease in TAC when compared to conventional flow sheet optimum designs. The applied optimization method and sensitivity analysis have proven to produce results at the global optimum. This method is both practical and easily applied to other systems, even to systems with more than three components.     

Simultaneous refolding,purification, and immobilization of recombinant Fibrobacter succinogenes 1,3‐1,4‐β‐D‐glucanase on artificial oil bodies

BACKGROUND: 1,3‐1,4‐β‐D‐glucanase (1,3‐1,4‐β‐D‐glucan 4‐glucanohydrolase; EC 3.2.1.73) has been used in a range of industrial processes. As a biocatalyst, it is better to use immobilized enzymes than free enzymes, therefore, the immobilization of 1,3‐1,4‐β‐D‐glucanase was investigated. RESULTS: A 1,3‐1,4‐β‐D‐glucanase gene from Fibrobacter succinogenes was overexpressed in Escherichia coli as a recombinant protein fused to the N terminus of oleosin, a unique structural protein of seed oil bodies. With the reconstitution of the artificial oil bodies (AOBs), refolding, purification, and immobilization of active 1,3‐1,4‐β‐D‐glucanase was accomplished simultaneously. Response surface modeling (RSM), with central composite design (CCD), and regression analysis were successfully applied to determine the optimal temperature and pH conditions of the AOB‐immobilized 1,3‐1,4‐β‐D‐glucanase. The optimal conditions for the highest immobilized 1,3‐1,4‐β‐D‐glucanase activity (7.1 IU mg^?1 of total protein) were observed at 39 °C and pH 8.8. Furthermore, AOB‐immobilized 1,3‐1,4‐β‐D‐glucanase retained more than 70% of its initial activity after 120 min at 39 °C, and it was easily and simply recovered from the surface of the solution by brief centrifugation; it could be reused eight times while retaining more than 80% of its activity. CONCLUSIONS: These results indicate that the AOB‐based system is a comparatively simple and effective method for simultaneous refolding, purification, and immobilization of 1,3‐1,4‐β‐D‐glucanase. Copyright © 2009 Society of Chemical Industry     

Practical improvements to autocovariance least‐squares

Identifying disturbance covariances from data is a critical step in estimator design and controller performance monitoring. Here, the autocovariance least‐squares (ALS) method for this identification is examined. For large industrial models with poorly observable states, the process noise covariance is high dimensional and the optimization problem is poorly conditioned. Also, weighting the least‐squares problem with the identity matrix does not provide minimum variance estimates. Here, ALS method to resolve these two challenges is modified. Poorly observable states using the singular value decomposition (SVD) of the observability matrix is identified and removed, thus decreasing the computational time. Using a new feasible‐generalized least‐squares estimator that approximates the optimal weighting from data, the variance of the estimates is significantly reduced. The new approach on industrial data sets provided by Praxair is successfully demonstrated. The disturbance model identified by the ALS method produces an estimator that performs optimally over a year‐long period. © 2015 American Institute of Chemical Engineers AIChE J, 61: 1840–1855, 2015     

Low‐order optimal regulation of parabolic PDEs with time‐dependent domain

Observer and optimal boundary control design for the objective of output tracking of a linear distributed parameter system given by a two‐dimensional (2‐D) parabolic partial differential equation with time‐varying domain is realized in this work. The transformation of boundary actuation to distributed control setting allows to represent the system's model in a standard evolutionary form. By exploring dynamical model evolution and generating data, a set of time‐varying empirical eigenfunctions that capture the dominant dynamics of the distributed system is found. This basis is used in Galerkin's method to accurately represent the distributed system as a finite‐dimensional plant in terms of a linear time‐varying system. This reduced‐order model enables synthesis of a linear optimal output tracking controller, as well as design of a state observer. Finally, numerical results are prepared for the optimal output tracking of a 2‐D model of the temperature distribution in Czochralski crystal growth process which has nontrivial geometry. © 2014 American Institute of Chemical Engineers AIChE J, 61: 494–502, 2015     

Integrated solvent and process design for continuous crystallization and solvent recycling using PC‐SAFT

Solvent usage is a major source of environmental waste in pharmaceutical industry. The current paradigm shift toward continuous manufacturing in pharmaceutical industry has renewed the interest in continuous crystallization, which offers the prospect of easy solvent recycling. However, the selection of solvents for an integrated crystallization processes is nontrivial due to the likely trade‐off between optimal solvent properties for crystallization and solvent separation and recycling. A systematic approach for the simultaneous optimization of process conditions and solvent selection for continuous crystallization including solvent recycling is presented. A unified perturbed‐chain statistical associating fluid theory model framework is applied to predict thermodynamic properties related to solubility and vapor‐liquid equilibrium, which is integrated with a process model. A continuous mapping procedure is adopted to solve the optimization problem effectively. A case study based on continuous antisolvent crystallization of paracetamol with solvent separation via flash demonstrates the approach. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1205–1216, 2018     

Data‐driven mathematical modeling and global optimization framework for entire petrochemical planning operations

In this work we develop a novel modeling and global optimization‐based planning formulation, which predicts product yields and properties for all of the production units within a highly integrated refinery‐petrochemical complex. Distillation is modeled using swing‐cut theory, while data‐based nonlinear models are developed for other processing units. The parameters of the postulated models are globally optimized based on a large data set of daily production. Property indices in blending units are linearly additive and they are calculated on a weight or volume basis. Binary variables are introduced to denote unit and operation modes selection. The planning model is a large‐scale non‐convex mixed integer nonlinear optimization model, which is solved to ε‐global optimality. Computational results for multiple case studies indicate that we achieve a significant profit increase (37–65%) using the proposed data‐driven global optimization framework. Finally, a user‐friendly interface is presented which enables automated updating of demand, specification, and cost parameters. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3020–3040, 2016     

Fluorescent DNA Labeling by N‐Mustard Analogues of S‐Adenosyl‐L‐Methionine

Azide and alkyne‐functionalized N‐mustard analogues of S‐adenosyl‐L ‐methionine have been synthesized and were demonstrated to undergo efficient methyltransferase‐dependent DNA alkylation by M.TaqI and M.HhaI. Subsequent labeling of the DNA with a fluorophore was carried out using copper‐catalyzed azide–alkyne cycloaddition chemistry and was visualized by fluorescence scanning. This work demonstrates the utility of functionalized N‐mustard analogues as biochemical tools to study biological methylation and offers a facile way to site‐selectively label substrates of DNA methyltransferases.     

Triplex‐Forming Twisted Intercalating Nucleic Acids (TINAs): Design Rules,Stabilization of Antiparallel DNA Triplexes and Inhibition of G‐Quartet‐Dependent Self‐Association

The majority of studies on DNA triple helices have been focused on pH‐sensitive parallel triplexes with Hoogsteen CT‐containing third strands that require protonation of cytosines. Reverse Hoogsteen GT/GA‐containing antiparallel triplex‐forming oligonucleotides (TFOs) do not require an acidic pH but their applicability in triplex technology is limited because of their tendency to form undesired highly stable aggregates such as G‐quadruplexes. In this study, G‐rich oligonucleotides containing 2–4 insertions of twisted intercalating nucleic acid (TINA) monomers are demonstrated to disrupt the formation of G‐quadruplexes and form stable antiparallel triplexes with target DNA duplexes. The structure of TINA‐incorporated oligonucleotides was optimized, the rules of their design were established and the optimal triplex‐forming oligonucleotides were selected. These oligonucleotides show high affinity towards a 16 bp homopurine model sequence from the HIV‐1 genome; dissociation constants as low as 160 nM are observed whereas the unmodified TFO does not show any triplex formation and instead forms an intermolecular G‐quadruplex with T_m exceeding 90 °C in the presence of 50 mM NaCl. Here we present a set of rules that help to reach the full potential of TINA‐TFOs and demonstrate the effect of TINA on the formation and stability of triple helical DNA.