Reliability of complex chemical engineering processes

This paper presents a stochastic performance modelling approach that can be used to optimise design and operational reliability of complex chemical engineering processes. The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice. An interface that facilitates automated two-way communication between Matlab^® and process simulation environment is used to generate large process responses. The resulting constrained optimisation problem is solved using both Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM); providing a wide range of stochastic process performance measures. Adding such capabilities to traditional deterministic process simulators provides a more informed basis for selecting optimum design factors; giving a simple way of enhancing overall process reliability and cost-efficiency. Two case study systems are considered to highlight the applicability and benefits of the approach.     

Cell-Free Synthesis of Selenoproteins in High Yield and Purity for Selective Protein Tagging

The selenol group of selenocysteine is much more nucleophilic than the thiol group of cysteine. Selenocysteine residues in proteins thus offer reactive points for rapid post-translational modification. Herein, we show that selenoproteins can be expressed in high yield and purity by cell-free protein synthesis by global substitution of cysteine by selenocysteine. Complete alkylation of solvent-exposed selenocysteine residues was achieved in 10 minutes with 4-chloromethylene dipicolinic acid (4Cl-MDPA) under conditions that left cysteine residues unchanged even after overnight incubation. Gd^III−Gd^III distances measured by double electron–electron resonance (DEER) experiments of maltose binding protein (MBP) containing two selenocysteine residues tagged with 4Cl-MDPA-Gd^III were indistinguishable from Gd^III−Gd^III distances measured of MBP containing cysteine reacted with 4Br-MDPA tags.     

Evolving an Accurate Decision Tree-Based Model for Predicting Carbon Dioxide Solubility in Polymers

Solubility is one of the most indispensable physicochemical properties determining the compatibility of components of a blending system. Research has been focused on the solubility of carbon dioxide in polymers as a significant application of green chemistry. To replace costly and time-consuming experiments, a novel solubility prediction model based on a decision tree, called the stochastic gradient boosting algorithm, was proposed to predict CO₂ solubility in 13 different polymers, based on 515 published experimental data lines. The results indicate that the proposed ensemble model is an effective method for predicting the CO₂ solubility in various polymers, with highly satisfactory performance and high efficiency. It produces more accurate outputs than other methods such as machine learning schemes and an equation of state approach.     

Characterization of a plasma polymer coating from an organophosphorus silane deposited at atmospheric pressure for fire-retardant purposes

Protective coatings from diethylphosphatoethyltriethoxysilane (DEPETS) have been deposited on different polymer substrates in a plasma discharge operated at atmospheric pressure. Plasma polymer chemistry and structure were characterized by means of Fourier transform infrared spectroscopy (FTIR), laser desorption ionization-mass spectrometry (LDI-MS), nuclear magnetic resonance (NMR) and scanning electron microscopy (SEM). A chemical structure of the plasma polymer has been proposed based on the coating molecular characterization. Coatings were deposited on polycarbonate (PC) and polyamide 6 (PA6) substrates. The flame retardant properties of coated substrate samples were assessed using cone calorimetry and compared to those of bare substrates. A significant increase in the time to ignition (TTI), up to +143%, was recorded after coating deposition due to the formation of a high-performance barrier layer at the surface of both polymer substrates.     

Control theory for stochastic distributed parameter systems,an engineering perspective

The main purpose of this paper is to survey some recent progresses on control theory for stochastic distributed parameter systems, i.e., systems governed by stochastic differential equations in infinite dimensions, typically by stochastic partial differential equations. We will explain the new phenomenon and difficulties in the study of controllability and optimal control problems for one dimensional stochastic parabolic equations and stochastic hyperbolic equations. In particular, we shall see that both the formulation of corresponding stochastic control problems and the tools to solve them may differ considerably from their deterministic/finite-dimensional counterparts. More importantly, one has to develop new tools, say, the stochastic transposition method introduced in our previous works, to solve some problems in this field.     

Optimal resilient power grid operation during the course of a progressing wildfire

We study a two-stage stochastic and nonlinear optimization model for operating a power grid exposed to a natural disaster. Although this approach can be generalized to any natural hazard of continuous (and not instantaneous) nature, our focus is on wildfires. We assume that an approaching wildfire impacts the power grid by reducing the transmission capacity of its overhead lines. At the time when proactive decisions have to be taken, the severity of the wildfire is not known. This introduces uncertainty. In this paper, we extend previous work by more realistically capturing this uncertainty and by strengthening the mathematical programming formulation through standard reformulation techniques. With these reformulation techniques, the resulting two-stage, convex mixed-integer quadratically constrained programming formulation can be efficiently solved using commercial quadratic programming solvers as demonstrated on a case study on a modified version of the IEEE 123-bus test system with 100 scenarios. We also quantify the uncertainties through a second case study using the following three standard metrics of two-stage stochastic optimization: the expected value of perfect information, the expected result of using the expected value solution and the value of the stochastic solution.     

CO2驱沥青质沉积对致密储层的伤害机理——以鄂尔多斯盆地延长组长8储层为例

为了揭示CO2驱替过程中沥青质沉积对致密储层的伤害机理,文中基于岩心核磁共振T2谱测试原理,开展了CO2注入压力下的岩心驱替实验,研究了沥青质在岩心中的沉积特征,评价了沥青质沉积对储层的伤害程度。实验结果表明:沥青质沉积量和渗透率伤害率随着CO2注入压力的升高呈现先快速上升后趋于平缓的趋势;在CO2注入过程中,沥青质主要沉积在弛豫时间大于10 ms的大、中孔隙,导致大、中孔隙占比下降,微细、小孔隙占比增加,且随着CO2注入压力的升高,大、中孔隙占比下降幅度增大,微细、小孔隙占比上升幅度增大;此外,沥青质沉积会引起润湿性发生反转,随着沥青质沉积量的增加,润湿反转指数增大,岩心润湿性不断向强油湿方向转变。     

Convergence analysis of the alternating RGLS algorithm for the identification of the reduced complexity Volterra model

In this paper we provide a convergence analysis of the alternating RGLS (Recursive Generalized Least Square) algorithm used for the identification of the reduced complexity Volterra model describing stochastic non-linear systems. The reduced Volterra model used is the 3rd order SVD-PARAFC-Volterra model provided using the Singular Value Decomposition (SVD) and the Parallel Factor (PARAFAC) tensor decomposition of the quadratic and the cubic kernels respectively of the classical Volterra model. The Alternating RGLS (ARGLS) algorithm consists on the execution of the classical RGLS algorithm in alternating way. The ARGLS convergence was proved using the Ordinary Differential Equation (ODE) method. It is noted that the algorithm convergence canno׳t be ensured when the disturbance acting on the system to be identified has specific features. The ARGLS algorithm is tested in simulations on a numerical example by satisfying the determined convergence conditions. To raise the elegies of the proposed algorithm, we proceed to its comparison with the classical Alternating Recursive Least Squares (ARLS) presented in the literature. The comparison has been built on a non-linear satellite channel and a benchmark system CSTR (Continuous Stirred Tank Reactor). Moreover the efficiency of the proposed identification approach is proved on an experimental Communicating Two Tank system (CTTS).     

The PAR(p) Interconfigurations model used by the Brazilian Electric Sector

This study discusses the characteristics of the Periodic Autoregressive model, PAR(p), which is used to generate synthetic series of inflow energies that serve as entries for computer platforms that implement the planning and expansion of the operations of the BES – the Brazilian Electric Sector (SEB – Sistema Elétrico Brasileiro). The methodology for the design of a generating plant is presented in addition to the fundamentals of the “PAR(p) Interconfigurations” Model, which is referred to as the Inflow Energy Generation Model (IEGM) in this study. The major contribution of this study is to provide the first scientific discussion of the representation of multiple configurations using the PAR(p) model. For this purpose, several topics related to the time series are discussed, such as the definition of the model order, the matter of stationarity and the need to address possible outliers. Finally, a case study is presented, wherein the results of the estimation and generation of the described model’s scenarios are demonstrated.