非线性观测器及其应用(上)一类高阶非线性系统的观测器

针对子系统的不可测状态之间呈下三角阵式关联的一类主阶非线性系统,本文给出了其观测器型式和反馈增益阵的设计方法,同时进一步讲座了存在不可测输入时系统的观测,这种观测器设计简单,便于实施和调变量进行的处理,对于化工生产过程中经常遇到的这类高阶非线性系统,这种观测器设计简单,便于实施和调整。     

Cointegrating regressions with messy regressors and an application to mixed‐frequency series

We consider a cointegrating regression in which the integrated regressors are messy in the sense that they contain data that may be mismeasured, missing, observed at mixed frequencies or have other irregularities that cause the econometrician to observe them with mildly nonstationary noise. Least squares estimation of the cointegrating vector is consistent. Existing prototypical variance‐based estimation techniques, such as canonical cointegrating regression, are both consistent and asymptotically mixed normal. This result is robust to weakly dependent but possibly nonstationary disturbances.     

Optimization-based process synthesis under seasonal and daily variability: Application to concentrating solar power

We propose an optimization-based framework for process synthesis under variability in two frequencies. Low-frequency variability is represented through scenarios and high-frequency variability is modeled using modes. The proposed framework allows for the selection of different process configurations during different modes, a feature necessary to model systems under wide high frequency variability (e.g., solar-based technologies). The optimization problem is formulated as a two-stage stochastic programming model with mode subproblems nested inside each scenario. The proposed framework is applied to the design of concentrating solar power plants with thermochemical energy storage, leading to the formulation of a computationally efficient model, as well as the identification of a superior design. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16458 2019     

Data envelopment analysis approach to targeting in sustainable chemical process design: Application to liquid fuels

This article presents a framework for combining data envelopment analysis with process systems engineering tools, aiming to improve the sustainability of chemical processes. Given a set of chemical processes, each characterized by performance indicators, the framework discriminates between efficient and inefficient processes in regard to these indicators. We develop an approach to quantifying the closest targets for an inefficient process to become efficient, while preventing unrealistic targets by accounting for thermodynamic limitations represented as mass and energy flow constraints. We demonstrate the capabilities of the framework by assessing a methanol production process with captured CO₂ and fossil-based H₂, in comparison to 10 alternatives. The methanol fuel is found to be suboptimal in comparison with other fuels. Making it competitive would require a significant (unrealistic in the short term) reduction in H₂ price. Alternatively, the methanol fuel could become competitive upon combining fossil-based H₂ with sustainably produced H₂ via wind-powered electrolysis. © 2018 American Institute of Chemical Engineers AIChE J, 65: e16480 2019     

On identification of well‐conditioned nonlinear systems: Application to economic model predictive control of nonlinear processes

The focus of this work is on economic model predictive control (EMPC) that utilizes well‐conditioned polynomial nonlinear state‐space (PNLSS) models for processes with nonlinear dynamics. Specifically, the article initially addresses the development of a nonlinear system identification technique for a broad class of nonlinear processes which leads to the construction of PNLSS dynamic models which are well‐conditioned over a broad region of process operation in the sense that they can be correctly integrated in real‐time using explicit numerical integration methods via time steps that are significantly larger than the ones required by nonlinear state‐space models identified via existing techniques. Working within the framework of PNLSS models, additional constraints are imposed in the identification procedure to ensure well‐conditioning of the identified nonlinear dynamic models. This development is key because it enables the design of Lyapunov‐based EMPC (LEMPC) systems for nonlinear processes using the well‐conditioned nonlinear models that can be readily implemented in real‐time as the computational burden required to compute the control actions within the process sampling period is reduced. A stability analysis for this LEMPC design is provided that guarantees closed‐loop stability of a process under certain conditions when an LEMPC based on a nonlinear empirical model is used. Finally, a classical chemical reactor example demonstrates both the system identification and LEMPC design techniques, and the significant advantages in terms of computation time reduction in LEMPC calculations when using the nonlinear empirical model. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3353–3373, 2015     

Novel operability‐based approach for process design and intensification: Application to a membrane reactor for direct methane aromatization

This article introduces a novel operability‐based approach for process design and intensification of energy systems described by nonlinear models. This approach is applied to a membrane reactor (MR) for the direct methane aromatization (DMA) conversion to benzene and hydrogen. The proposed method broadens the scope of the traditional path of the operability approaches for design and control, mainly oriented to obtain the achievable output set (AOS) from the available input set, and compare the computed AOS to a desired output set. In particular, an optimization algorithm based on nonlinear programming tools is formulated for the calculation of the desired input set that is feasible considering process constraints and intensification targets. Results on the application of the operability method as a tool for process intensification show reduction of the DMA‐MR footprint (≈77% reactor volume and 80% membrane area reduction) for an equivalent level of performance, when compared to the base case. This case study indicates that the novel approach can be a powerful tool for process intensification of membrane reactors and other complex chemical processes. © 2016 American Institute of Chemical Engineers AIChE J, 63: 975–983, 2017     

Using functional programming to recognize named structure in an optimization problem: Application to pooling

Branch‐and‐cut optimization solvers typically apply generic algorithms, e.g., cutting planes or primal heuristics, to expedite performance for many mathematical optimization problems. But solver software receives an input optimization problem as vectors of equations and constraints containing no structural information. This article proposes automatically detecting named special structure using the pattern matching features of functional programming. Specifically, we deduce the industrially‐relevant nonconvex nonlinear Pooling Problem within a mixed‐integer nonlinear optimization problem and show that we can uncover pooling structure in optimization problems which are not pooling problems. Previous work has shown that preprocessing heuristics can find network structures; we show that we can additionally detect nonlinear pooling patterns. Finding named structures allows us to apply, to generic optimization problems, cutting planes or primal heuristics developed for the named structure. To demonstrate the recognition algorithm, we use the recognized structure to apply primal heuristics to a test set of standard pooling problems. © 2016 The Authors AIChE Journal published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers AIChE J, 62: 3085–3095, 2016