New Approach in Modeling of Metallocene‐Catalyzed Olefin Polymerization Using Artificial Neural Networks

A new approach for the estimation of kinetic rate constants in olefin polymerization using metallocene catalysts is presented. The polymerization rate has been modeled using the method of moments. An ANN has been used and trained to behave like the mathematical model developed before, so that it gets polymerization rate at different reaction times and predicts reaction rate constants. The network was trained using modeling results in desired operational window. The polymerization rates were normalized to make the network work independent of operational conditions. The model has also been applied to real polymerization rate data and the predictions were satisfactory. This model is specially useful in comparing different new metallocene catalysts.

     

Model Reduction in Emulsion Polymerization Using Hybrid First‐Principles/Artificial Neural Network Models

A first‐principles mathematical model for emulsion polymerization was reduced by using a hybrid mathematical model composed by artificial neural networks (ANN) and material balances. The goal was to have an accurate model that may be integrated fast enough to be used for online optimization purposes. In the reduced model the polymerization rate and the instantaneous weight‐average molecular weight were calculated by means of artificial neural networks. These ANNs were incorporated to first‐principles material balances. The accuracy of the reduced model under a wide range of conditions was assessed. Savings in computer time were achieved by using the reduced model, which makes it suitable for online optimization purposes.

Effect of the temperature on the cumulative weight‐average molecular weight: first principles mathematical model (—); (ANN2) and hybrid model predictions: (▵) 50 °C, (▪) 60 °C^(training), (▿) 70 °C^(validation), (•) 80 °C, (○) 90 °C.     

Model Reduction in Emulsion Polymerization Using Hybrid First Principles/Artificial Neural Networks Models, 2

Summary: A “series” hybrid model based on material balances and artificial neural networks to predict the evolution of weight average molecular weight, , in semicontinuous emulsion polymerization with long chain branching kinetics is presented. The core of the model is composed by two artificial neural networks (ANNs) that calculate polymerization rate, R_p, and instantaneous weight‐average molecular weight, from reactor process variables. The subsequent integration of the material balances allowed to obtain the time evolution of conversion and , along the polymerization process. The accuracy of the proposed model under a wide range of conditions was assessed. The low computer‐time load makes the hybrid model suitable for optimization strategies.

Effect of the monomer feed rate on .     

卤代化合物的碳—13化学位移模拟

用人工神经网络和多元回归方法对含２个碳的２１个卤代化合物的３５个化学位移进行计算机图像模拟，结果表明，人工神经网络方法优于多元回归方法，同时此种方法处理这类问题有明显的优势，波谱模拟技术在有机化合物结构解析中是非常有用的方法。     

An Emulsifier‐Free RAFT‐Mediated Process for the Efficient Synthesis of Cerium Oxide/Polymer Hybrid Latexes

Hybrid latexes based on cerium oxide nanoparticles are synthesized via an emulsifier‐free process of emulsion polymerization employing amphiphatic macro‐RAFT agents. Poly(butyl acrylate‐co‐acrylic acid) random oligomers of various compositions and chain lengths are first obtained by RAFT copolymerization in the presence of a trithiocarbonate as controlling agent. In a second step, the seeded emulsion copolymerization of styrene and methyl acrylate is carried out in the presence of nanoceria with macro‐RAFT agents adsorbed at their surface, resulting in a high incorporation efficiency of cerium oxide nanoparticles in the final hybrid latexes, as evidenced by cryo‐transmission electron microscopy.     

Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study

Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological functions. Understanding PPIs reveals how cells behave and operate, such as the antigen recognition and signal transduction in the immune system. In the past decades, many computational methods have been developed to predict PPIs automatically, requiring less time and resources than experimental techniques. In this paper, we present a comparative study of various graph neural networks for protein–protein interaction prediction. Five network models are analyzed and compared, including neural networks (NN), graph convolutional neural networks (GCN), graph attention networks (GAT), hyperbolic neural networks (HNN), and hyperbolic graph convolutions (HGCN). By utilizing the protein sequence information, all of these models can predict the interaction between proteins. Fourteen PPI datasets are extracted and utilized to compare the prediction performance of all these methods. The experimental results show that hyperbolic graph neural networks tend to have a better performance than the other methods on the protein-related datasets.     

Resolution of Differential Pulse Voltammetric Peaks Using Genetic Algorithm Based Variable Selection‐Partial Least Squares and Principal Component‐Artificial Neural Networks

Differential Pulse Voltammetry has been used for the simultaneous determination of cysteine, tyrosine and trptophan on the unmodified glassy carbon electrode. In the analysis of these analytes in the same samples, the main difficulty is the high degree of overlapping of voltammograms. The relationships between the currents and the concentrations are complex and highly nonlinear. The predictive ability of principal component regression (PCR), partial least squares regression (PLS), genetic algorithm‐partial least squares regression (GA‐PLS) and principal component‐artificial neural networks (PC‐ANNs) were examined for simultaneous determination of three amino acids. For a regression model, everything that could not help in constructing the model may be considered as noise without further specification. PC‐ANN and GA‐PLS use significant data and show superiority over other applied multivariate methods. The proposed method was also applied satisfactorily to determination of analytes in some synthetic samples.     

A Self‐Reporting Photocatalyst for Online Fluorescence Monitoring of High Throughput RAFT Polymerization

Translating controlled/living radical polymerization (CLRP) from batch to the high throughput production of polymer libraries presents several challenges in terms of both polymer synthesis and characterization. Although recently there have been significant advances in the field of low volume, high throughput CLRP, techniques able to simultaneously monitor multiple polymerizations in an “online” manner have not yet been developed. Here, we report our discovery that 5,10,15,20‐tetraphenyl‐21H,23H‐porphine zinc (ZnTPP) is a self‐reporting photocatalyst that can mediate PET‐RAFT polymerization as well as report on monomer conversion via changes in its fluorescence properties. This enables the use of a microplate reader to conduct high throughput “online” monitoring of PET‐RAFT polymerizations performed directly in 384‐well, low volume microtiter plates.     

Estimation of Polymerization Conditions Needed to Make Ethylene/1‐olefin Copolymers with Specific Microstructures Using Artificial Neural Networks

Two artificial neural network models (forward and inverse) are developed to describe ethylene/1‐olefin copolymerization with a catalyst having two site types using training and testing datasets obtained from a polymerization kinetic model. The forward model is applied to predict the molecular weight and chemical composition distributions of the polymer from a set of polymerization conditions, such as ethylene concentration, 1‐olefin concentration, cocatalyst concentration, hydrogen concentration, and polymerization temperature. The results of the forward model agree well with those from the kinetic model. The inverse model is applied to determine the polymerization conditions to produce polymers with desired microstructures. Although the inverse model generates multiple solutions for the general case, unique solutions are obtained when one of the three key process parameters (ethylene concentration, 1‐olefin concentration, and polymerization temperature) is kept constant. The proposed model can be used as an efficient tool to design materials from a set of polymerization conditions.

     

Radial Basis Function Neural Networks-Based Modeling of the Membrane Separation Process： Hydrogen Recovery from Refinery Gases

Membrane technology has found wide applications in the petrochemical industry, mainly in the purification and recovery of the hydrogen resources. Accurate prediction of the membrane separation performance plays an important role in carrying out advanced process control (APC). For the first time, a soft-sensor model for the membrane separation process has been established based on the radial basis function (RBF) neural networks. The main performance parameters, i.e, permeate hydrogen concentration, permeate gas flux, and residue hydrogen concentration, are estimated quantitatively by measuring the operating temperature, feed-side pressure, permeate-side pressure, residue-side pressure, feed-gas flux, and feed-hydrogen concentration excluding flow structure, membrane parameters, and other compositions. The predicted results can gain the desired effects. The effectiveness of this novel approach lays a foundation for integrating control technology and optimizing the operation of the gas membrane separation process.     

Nitroxide‐Mediated Radical Polymerization in Miniemulsion at Stationary State: Rationale for Independence of Polymerization Rate on Nitroxide Partitioning Using Oil‐Phase Initiation

Summary: Simulations based on the kinetics and mechanism of nitroxide‐mediated free radical polymerization (NMP) have been carried out in order to understand the hitherto largely unexplained effects (or lack thereof) of nitroxide partitioning in aqueous miniemulsion NMP. The focus has been on the miniemulsion NMP of styrene mediated by TEMPO and 4‐hydroxy‐TEMPO, two nitroxides with very similar activation‐deactivation equilibria, but very different organic phase‐aqueous phase partition coefficients. The general conclusion is that the organic phase propagating radical and nitroxide concentrations are unaffected by the partition coefficient in the stationary state, but the rate of polymerization and the extent of bimolecular termination increase with increasing nitroxide water solubility in the pre‐stationary state region. Specific NMP systems are, therefore, affected differently by nitroxide partitioning depending on whether polymerization predominantly occurs in the stationary state or not, which in turn is governed mainly by the activation‐deactivation equilibrium constant and the rate of thermal initiation.

Simulated organic‐phase propagating radical concentrations in the presence of thermal initiation for TEMPO‐mediated miniemulsion free radical polymerization of styrene for different nitroxide partitioning coefficients at 125 °C.     

On the Robustness of Forward and Inverse Artificial Neural Networks for the Simulation of Ethylene/1‐Butene Copolymerization

Forward and inverse artificial neural network (ANN) models are used to describe ethylene/1‐butene copolymerization with a model catalyst having two site types. The forward ANN predicts number and weight average molecular weights, average comonomer content, and polymer yield as a function of a set of polymerization conditions, while the inverse model estimates polymerization conditions needed to produce copolymers with desired microstructures. The forward model is found to be robust and resilient to random noise introduced into the datasets. The inverse model, however, leads to multiple solutions (several polymerization conditions can produce polymers with similar microstructures) and is sensitive to random noise in the data. Although the polymerization conditions estimated from inverse ANN are different from the model data, the estimated polymerization conditions are found to provide similar microstructures even with the random noise.     

Haruspex: A Neural Network for the Automatic Identification of Oligonucleotides and Protein Secondary Structure in Cryo‐Electron Microscopy Maps

In recent years, three‐dimensional density maps reconstructed from single particle images obtained by electron cryo‐microscopy (cryo‐EM) have reached unprecedented resolution. However, map interpretation can be challenging, in particular if the constituting structures require de‐novo model building or are very mobile. Herein, we demonstrate the potential of convolutional neural networks for the annotation of cryo‐EM maps: our network Haruspex has been trained on a carefully curated set of 293 experimentally derived reconstruction maps to automatically annotate RNA/DNA as well as protein secondary structure elements. It can be straightforwardly applied to newly reconstructed maps in order to support domain placement or as a starting point for main‐chain placement. Due to its high recall and precision rates of 95.1 % and 80.3 %, respectively, on an independent test set of 122 maps, it can also be used for validation during model building. The trained network will be available as part of the CCP‐EM suite.     

Use of an Electronic Tongue Based on All‐Solid‐State Potentiometric Sensors for the Quantitation of Alkaline Ions

An array of eight nonspecific potentiometric sensors was used in combination with multivariate calibration for the simultaneous determination of NH , K⁺ and Na⁺ ions. The sensors were of the all‐solid‐state type and employed a PVC polymer membrane. Signals were processed by using a multilayer artificial neural network (ANN). The ANN configuration used was optimized by using 8 neurons in the input layer, 5 in the hidden layer and 3 in the output layer. Use of the Bayesian Regularization algorithm allowed a quick building of an accurate model, as confirmed by random multi‐starting of network weights. The system was used to analyze synthetic and river water, waste water and fertilizer samples. Correct results were obtained for the three ions in synthetic and real water samples; in fertilizers, ammonium ion can be determined, while sodium and potassium show biased results.     

Design of Nonlinear Model‐Based Control Using Bifurcation Analysis for Solution Polymerizations Carried Out in Lumped‐Distributed Reactors

In order to implement nonlinear control, nonlinear system identification must be performed, however, there are open questions concerning this field of process control, for example, experimental planning, model structure selection, parameter estimation, and validation. Therefore, the study of nonlinear model identification is a relevant unsolved problem that needs to be handled for nonlinear control synthesis. This paper presents the use of bifurcation theory, dynamic and stability analysis for nonlinear identification, and control of polymerization reactors. Peroxide‐initiated styrene‐solution polymerization reactors (lumped‐distributed) are investigated: batch, continuous stirred‐tank reactor (CSTR), and tubular reactors. Open and closed loop analyses are carried out using jacket temperature and weight average molecular weight setpoints as the bifurcation parameters. Phenomenological mathematical models, neural network nonlinear models, and an experimental data from a polymerization unit are employed for validating the proposed methodology in order to implement confident nonlinear controllers.     

Boiling Point of a Set of Alkanes,Alcohols and Chloroalkanes: QSAR with Atom Type Electrotopological State Indices Using Artificial Neural Networks

Abstract

The normal boiling point is modeled for a set of 372 saturated compounds, including 154 alkanes, 108 alcohols and 110 (poly)chloroalkanes. The newly introduced atom type electrotopological state indices serve as the structure variables and artificial neural networks (with back propagation of error) are used for the analysis. A network with a 6:7:1 architecture produces an average relative error of 0.97% for the whole data set, including the 21% of the data used as the test set. The mean absolute error (MAE) for this model is 4.00 for the whole set, corresponding to an rms error of 5.41; for the test set the MAE is 4.03 with an rms of 5.23. The low error on the test set indicates that this model has predictive power.     

Pesticide‐conjugated polyacrylate nanoparticles: novel opportunities for improving the photostability of emamectin benzoate

Emamectin benzoate, a macrocyclic lactone, can be used in low quantities to control arthropod pests, effectively. However, its poor photostability prevents its further use. To delay its photodegradation, novel acrylate‐type polymeric nanoparticles were synthesized and tested as materials for improving pesticide photostability. N‐acylated emamectin benzoate was synthesized via bonding emamectin benzoate to acrylamide. The resulting pesticide, containing the double bond linkage –C=C–N–, was copolymerized with butyl acrylate and methyl methacrylate by the emulsion polymerization method. The refined polymers were characterized by Fourier transform infrared spectroscopy spectroscopy, and result illustrated the pesticide was conjugated to the polymers. Atomic force microscope and dynamic light scattering analyses were also used for determining the average particle diameters of pesticide–polymer conjugates. Photostability tests showed that the nanoparticles obtained exhibited greatly improved photostability. Additionally, the laboratory toxicity tests demonstrated that the insecticidal effects of the novel emamectin benzoate formulation were better than those of the control pesticide formulation (emamectin benzoate EC). Copyright © 2012 John Wiley & Sons, Ltd.