Modelling of a post-combustion CO₂ capture process using neural networks

This paper presents a study of modelling post-combustion CO₂ capture process using bootstrap aggregated neural networks. The neural network models predict CO₂ capture rate and CO₂ capture level using the following variables as model inputs: inlet flue gas flow rate, CO₂ concentration in inlet flue gas, pressure of flue gas, temperature of flue gas, lean solvent flow rate, MEA concentration and temperature of lean solvent. In order to enhance model accuracy and reliability, multiple feedforward neural network models are developed from bootstrap re-sampling replications of the original training data and are combined. Bootstrap aggregated model can offer more accurate predictions than a single neural network, as well as provide model prediction confidence bounds. Simulated CO₂ capture process operation data from gPROMS simulation are used to build and verify neural network models. Both neural network static and dynamic models are developed and they offer accurate predictions on unseen validation data. The developed neural network models can then be used in the optimisation of the CO₂ capture process

Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201

2,2′,4,4′,6,6′-Hexamethyl-N-(3-phthalimidoprop­yl)-N,N′-(propane-1,3-di­yl)dibenzene­sulfonamide

In the title compound, C32H38N3O6S2, an inter­mediate in the synthesis of polyamine drugs, the dihedral angle between the phenyl rings of the two 2,4,6-trimethyl­benzene­sulfonyl groups is 27.1 (3)°. In the crystal structure, mol­ecules are linked by inter­molecular N—H⋯O hydrogen bonds, thereby forming an infinite one-dimensional chain propagating along [010]

N,N′-Bis(2-cyano­ethyl)-4,4′-dimethyl-N,N′-(butane-1,4-di­yl)dibenzene­sulfonamide

The complete mol­ecule of the title compound, C24H30N4O4S2, is generated by a crystallographic inversion centre. In the crystal, weak C—H⋯O inter­actions link the mol­ecules, forming infinite sheets

2-(3-Bromo­prop­yl)isoindoline-1,3-dione

In the title compound, C11H10BrNO2, the dihedral angle between the five- and six-membered rings of the phthalamide system is 1.00 (16)°. There are no significant inter­molecular inter­ations except for van der Waals contacts