Epoxidized perfluoropolyethers: A route to hydrophobic,negative‐tone photoresists

The synthesis, formulation, and wafer level processing conditions of a heavily fluorinated hydrophobic photoresist was demonstrated. The synthesis is based on terminal epoxy modification of commercially available perfluoropolyethers. Structural characterization shows that terminal epoxide can open during the synthetic process, but in a simple formulation has a negligible effect on photoresolution of the photoresist. Formulation into a traditional photoresist requires careful selection of appropriate cosolvents to ensure solubility of the hydrophobic epoxy and hydrophilic photoacid generator while attaining adequate coating quality. Formulation processing conditions are presented and the chemical resistance of the resist through aggressive processing steps is demonstrated. Wafer level patterning using traditional photolithographic tools illustrates the applicability of the formulation and process conditions for traditional resist or microfluidic applications. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Process synthesis using block superstructure with automated flowsheet generation and optimization

An alternative method for chemical process synthesis using a block‐based superstructure representation is proposed. The block‐based superstructure is a collection of blocks arranged in a two‐dimensional grid. The assignment of different equipment on blocks and the determination of their connectivity are performed using a mixed‐integer nonlinear formulation for automated flowsheet generation and optimization‐based process synthesis. Based on the special structure of the block representation, an efficient strategy is proposed to generate and successively refine feasible and optimized process flowsheets. Our approach is demonstrated using two process synthesis case studies adapted from the literature and one new process synthesis problem for methanol production from biogas © 2018 American Institute of Chemical Engineers AIChE J, 64: 3082–3100, 2018     

Rigid PVC formulation optimization using sequential simplex

A rigid PVC formulation was optimized for processing window and cost using sequential simplex techniques in combination with desirability functions. This optimization was performed on seven of the ten ingredients from a ‘standard’ siding compound with a relatively limited number of experiments. The processing window contour mapping technique was used to evaluate the effects of formulation changes. This was combined with the formulation cost by using desirability functions to give an overall response for the simplex to optimize. The basic mechanics of sequential simplex and desirability functions are described along with the results of the optimization.     

Solving Multi‐objective MILP Problems in Process Synthesis using the Multi‐Criteria Branch and Bound Algorithm

The paper briefly describes the problem of process synthesis in the area of chemical engineering, and suggests its formulation as a Multi‐Objective Programming problem. Process synthesis optimization is usually modeled as Mixed Integer Linear Programming (MILP) or Mixed Integer Non‐Linear Programming (MINLP) with an economic objective function. We claim that incorporating more criteria (e.g., environmental criteria) in this kind of combinatorial optimization problem offers the decision makers the opportunity to refine their final decision by examining more than one solution (a set of efficient or Pareto optimal solutions instead of one optimal solution). For solving the multi‐objective process synthesis problem, an improved version of the Multi‐Criteria Branch and Bound (MCBB) algorithm, which has been developed by the same authors, is used. MCBB is a vector maximization algorithm capable of deriving all efficient points (supported and unsupported), for small and medium sized Multi‐Objective MILP problems. The application of MCBB in two examples from process synthesis is also presented.     

Simultaneous process synthesis and control design under uncertainty: A worst‐case performance approach

A new methodology that includes process synthesis and control structure decisions for the optimal process and control design of dynamic systems under uncertainty is presented. The method integrates dynamic flexibility and dynamic feasibility in a single optimization formulation, thus, reducing the costs to assess the optimal design. A robust stability test is also included in the proposed method to ensure that the optimal design is stable in the presence of magnitude‐bounded perturbations. Since disturbances are treated as stochastic time‐discrete unmeasured inputs, the optimal process synthesis and control design specified by this method remains feasible and stable in the presence of the most critical realizations in the disturbances. The proposed methodology has been applied to simultaneously design and control a system of CSTRs and a ternary distillation column. A study on the computational costs associated with this method is presented and compared to that required by a dynamic optimization‐based scheme. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2497–2514, 2013     

Synthesis of Multi-component Mass-exchange Networks

This paper presents a superstructure-based formulation for the synthesis of mass-exchange networks （MENs） considering multiple components. The superstructure is simplified by directly using the mass separation agents （MSA） from their sources, and therefore the automatic synthesis of the multi-component system involved in the MENs can be achieved without choosing a ＇key-component＇ either for the whole process or the mass exchangers A mathematical model is proposed to carry out the optimization process. The concentrations, flow rates, matches and unit operation displayed in the obtained network constitute the exact representation of the mass exchange process in terms of all species in the system. An example is used to illustrate and demonstrate the application of the proposed method.     

A framework for efficient large scale equation-oriented flowsheet optimization

Despite the economic benefits of flowsheet optimization, many commercial tools suffer from long computational times, limited problem formulation flexibility and numerical instabilities. In this study, we address these challenges and present a framework for efficient large scale flowsheet optimization. This framework couples advanced process optimization formulations with state-of-the-art algorithms, and includes several notable features such as (1) an optimization-friendly formulation of cubic equation of state thermodynamic models; (2) a new model for distillation column optimization based on rigorous mass, equilibrium, summation and heat (MESH) equations with a variable number of trays that avoids integer variables; (3) improvements on the Duran–Grossmann formulation for simultaneous heat integration and flowsheet optimization; and (4) a systematic initialization procedure based on model refinements and a tailored multi-start algorithm to improve feasibility and identify high quality local solutions.Capabilities of the framework are demonstrated on a cryogenic air separation unit synthesis study, including two thermally coupled distillation columns and accompanying multistream heat exchangers. A superstructure is formulated that includes several common ASU configurations in literature. As part of the optimization problem the solver selects the best topology in addition to operating conditions (temperatures, flowrates, etc.) for coal oxycombustion applications. The optimization problem includes up to 16,000 variables and 500 degrees of freedom, and predicts specific energy requirement of 0.18 to 0.25 kWh/kg of O₂ depending on design assumptions. These results are compared to literature and plans to extend the framework to an entire coal oxycombustion power plant optimization study are discussed.     

Energy demand management for process systems through production scheduling and control

Demand response (DR) is an integral part of the Smart Grid paradigm, and has become the focus of growing research, development, and deployment in residential, commercial and industrial systems over the last few years. In process systems, energy demand management through production scheduling is an increasingly important tool that has the potential to provide significant economic and operational benefits by promoting the responsiveness of the process operation and its interactions with the utility providers. However, the dynamic behavior of the underlying process, especially during process transitions, is seldom taken into account as part of the DR problem formulation. Furthermore, the incorporation of energy constraints related to electricity pricing and energy resource availability presents an additional challenge. The goal of this study is to present a novel optimization formulation for energy demand management in process systems that accounts explicitly for transition behaviors and costs, subject to time‐sensitive electricity prices and uncertainties in renewable energy resources. The proposed formulation brings together production scheduling and closed‐loop control, and is realized through a real‐time or receding‐horizon optimization framework depending on the underlying operational scenarios. The dynamic formulation is cast as a mixed‐integer nonlinear programming problem based on a proposed discretization approach, and its merits are demonstrated using a simulated continuous stirred tank reactor where the energy required is assumed to be roughly proportional to the material flow. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3756–3769, 2015     

紫外光固化涂料—液态光致抗蚀剂

简单介绍了紫外光固化的原理 ,紫外光固化涂料的配方构成、固化设备及原材料的选择 ,并举例描述了液态光致抗蚀剂的组成和特性。     

Synthesis of heat exchanger networks at subambient conditions with compression and expansion of process streams

This article presents an optimization formulation for the synthesis of heat exchanger networks where pressure levels of process streams can be adjusted to improve heat integration. Especially important at subambient conditions, this allows for the interconversion of work, temperature, and pressure‐based exergy and leads to reduced usage of expensive cold utility. Furthermore, stream temperatures and pressures are tuned for close tracking of the composite curves yielding increased exergy efficiency. The formulation is showcased on a simple example and applied to a case study drawn from the design of an offshore natural gas liquefaction process. Aided by the optimization, it is demonstrated how the process can extract exergy from liquid nitrogen and carbon dioxide streams to support the liquefaction of a natural gas stream without additional utilities. This process is part of a liquefied energy chain, which, supplies natural gas for power generation while facilitating carbon dioxide sequestration. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Simultaneous mixed-integer dynamic scheduling of processes and their energy systems

Increasingly volatile electricity prices make simultaneous scheduling optimization desirable for production processes and their energy systems. Simultaneous scheduling needs to account for both process dynamics and binary on/off-decisions in the energy system leading to challenging mixed-integer dynamic optimization problems. We propose an efficient scheduling formulation consisting of three parts: a linear scale-bridging model for the closed-loop process output dynamics, a data-driven model for the process energy demand, and a mixed-integer linear model for the energy system. Process dynamics is discretized by collocation yielding a mixed-integer linear programming (MILP) formulation. We apply the scheduling method to three case studies: a multiproduct reactor, a single-product reactor, and a single-product distillation column, demonstrating the applicability to multiple input multiple output processes. For the first two case studies, we can compare our approach to nonlinear optimization and capture 82% and 95% of the improvement. The MILP formulation achieves optimization runtimes sufficiently fast for real-time scheduling.     

A modeling strategy for integrated batch process development based on mixed-logic dynamic optimization

This paper introduces an optimization-based approach for the simultaneous solution of batch process synthesis and plant allocation, with decisions like the selection of chemicals, process stages, task-unit assignments, operating modes, and optimal control profiles, among others. The modeling strategy is based on the representation of structural alternatives in a state-equipment network (SEN) and its formulation as a mixed-logic dynamic optimization (MLDO) problem. Particularly, the disjunctive multistage modeling strategy by Oldenburg and Marquardt (2008) is extended to combine and organize single-stage and multistage models for representing the sequence of continuous and batch units in each structural alternative and for synchronizing dynamic profiles in input and output operations with material transference. Two numerical examples illustrate the application of the proposed methodology, showing the enhancement of the adaptability potential of batch plants and the improvement of global process performance thanks to the quantification of interactions between process synthesis and plant allocation decisions.     

Optimization of Membrane Formulation and Process Variables via Crossed-Design Concept in Design of Experimental (DOE)

Abstract

A mixture-process design methodology, i.e., the crossed design, is proposed for experimental analysis and optimization. Five mixture materials for membrane formulation and two process factors for casting condition were fixed in the design methodology. The study was to generate a regression model for each of the responses, based on the experimental data and analysis variance of the study. Based on the response models, the optimal blend and casting condition were predicted. The highest desirability function, D, which prevailed from the optimization was 0.66. This optimal blend composition of the cast solution and the process condition in crossed design is proven to increase the membrane performance through the high membrane porosity, the high protein binding ability, and the fast lateral wicking rate.     

不确定性下基于多工况优化的可控性换热器网络综合

换热器网络在化工生产过程中起着能量回收、节能降耗的重要作用。常规换热器网络综合方法以经济性为目标进行参数和结构优化,导致换热设备之间的强烈耦合从而引起潜在的控制困难。此外,在实际生产中,无论是单个换热设备还是整个网络都不可避免地受到一些不确定性因素的干扰,故能够抵御这些扰动的可控性换热器网络是至关重要的。通过考虑网络结构与可控性之间的相互作用,提出可控性换热器网络综合方法,并基于多工况优化探究了决策变量对网络结构的影响。然后以柔性指数衡量扰动存在情况下换热器网络操作可行性,进一步优化换热器网络结构以抵御不确定性因素的干扰。最后,通过算例分析验证了该方法的可行性与有效性。     

Statistical optimization and characterization of pH-independent extended-release drug delivery of cefpodoxime proxetil using Box–Behnken design

The aim of this study was to develop and optimize the pH-independent extended-release (ER) formulations of cefpodoxime proxetil (CP) using response surface methodology by employing a 3-factor, 3-level Box–Behnken statistical design. The independent variables studied were the amount of hydroxypropyl methylcellulose (HPMC K4M), sodium alginate (SA) and microcrystalline cellulose (MCC). The drug release percent at 2, 4, 8, 14 and 24 h were the target responses and were restricted to 15–30, 35–55, 55–75, 75–90 and 90–110%, respectively. The response surface methodology and multiple response optimizations utilizing a polynomial equation were used to search for the optimal formulation with a specific release rate at different time intervals. The results showed that the effect of combination of HPMC K4M and SA was the most influencing factor on the drug release from ER matrix tablets. The mechanism of drug release from ER tablets was dependent on the added amount of SA. Validation of the optimization technique demonstrated the reliability of the model. The optimized formulation containing 25% HPMC, 15.14% SA, and 20.14% MCC was prepared according to the software determined levels. DSC and FTIR studies combined with the stability study of the optimized formulation proved the integrity of the developed formulation. The Box–Behnken experimental design facilitated the formulation and optimization of extended release hydrophilic matrix systems of CP in a short period of time and with the fewest number of experiments.     

混料设计及调优软件在热熔胶配方实验中的应用

根据常规试验方法和混料设计及调优软件指导两种方法分别进行了两种聚酰胺热熔胶的合成实验 ,实验结果说明用混料设计及调优软件指导的试验只用 12次就得到常规试验方法 3 1次得到的结果     

联醇技术流程模拟优化分析探讨

对甲醇合成工艺进行了评述,提出联醇法甲醇合成工艺重要意义。采用管壳式合成塔,使用C207型甲醇合成催化剂,建立联醇法甲醇合成工艺系统模型,借助化工稳态模拟软件,对甲醇合成流程进行了全流程模拟,得到该流程的基本工艺数据。利用化工稳态软件中的灵敏度分析工具从循环量变化、原料气组成、原料气中的甲醇含量等方面论述了操作条件变化对甲醇合成的影响,并进行优化研究。     

Mathematical programming approaches to the synthesis of chemical process systems

This paper presents a review of advances that have taken place in the mathematical programming approach to process design and synthesis. A review is first presented on the algorithms that are available for solving MINLP problems, and its most recent variant, Generalized Disjunctive Programming models. The formulation of superstructures, models and solution strategies is also discussed for the effective solution of the corresponding optimization problems. The rest of the paper is devoted to reviewing recent mathematical programming models for the synthesis of reactor networks, distillation sequences, heat exchanger networks, mass exchanger networks, utility plants, and total flowsheets. As will be seen from this review, the progress that has been achieved in this area over the last decade is very significant.     

响应面法优化N-氨基甲酰谷氨酸的合成工艺

关于N-氨基甲酰谷氨酸的合成国内外已有许多报道,通过以L-谷氨酸和尿素为原料,研究了N-氨基甲酰谷氨酸的合成工艺。在单因素实验的基础上,采用响应面分析法的Box-Behnken设计方法,利用软件Design-Expert 10考察了反应温度、反应时间、物料比等因素以及因素之间的交互作用对合成N-氨基甲酰谷氨酸产率的影响。建立了N-氨基甲酰谷氨酸产率的二次线性回归方程,获得了最优工艺条件:反应时间2.05 h、反应温度119.83℃、n(L-谷氨酸)∶n(尿素)=1∶2.2,在此条件下N-氨基甲酰谷氨酸产率为82.47%,并进行了3次验证性实验,得到N-氨基甲酰谷氨酸的产率为81.76%,与理论预测值相对误差较小,与响应面模型预测值非常吻合,说明采用响应面优化方案有效、可靠。     

Optimization of a Process Synthesis Superstructure Using an Ant Colony Algorithm

The optimization of chemical syntheses based on superstructure modeling is a perfect way for achieving the optimal plant design. However, the combinatorial optimization problem arising from this method is very difficult to solve, particularly for the entire plant. Relevant literature has focused on the use of mathematical programming approaches. Some research has also been conducted based on meta‐heuristic algorithms. In this paper, two approaches are presented to optimize process synthesis superstructure. Firstly, mathematical formulation of a superstructure model is presented. Then, an ant colony algorithm is proposed for solving this nonlinear combinatorial problem. In order to ensure that all the constraints are satisfied, an adaptive, feasible bound for each variable is defined to limit the search space. Adaptation of these bounds is executed by the suggested bound updating rule. Finally, the capability of the proposed algorithm is compared with the conventional Branch and Bound method by a case study.