THE USAGE OF ARTIFICIAL NEURAL NETWORKS IN HYDRODYNAMIC ANALYSIS OF FLOATING OFFSHORE PLATFORMS
Floating offshore structures, particularly floating oil production, storage and offloading systems (FPSOs) are still in great demand, both in small and large reservoirs, for deployment in deep water. The prediction of such vessels’ responses to her environmental loading over her lifetime is now often undertaken using response-based design methodology, although the approach is still in its early stages of development. Determining the vessel’s responses to hydrodynamic loads induced by long term sea environments is essential for implementing this approach effectively. However, it is often not practical to perform a complete simulation for every 3-hour period of environmental data being considered. Therefore, an Artificial Neural Networks (ANN) modelling technique has been developed for the prediction of FPSO’s responses to arbitrary wind, wave and current loads that alleviates this problem. Comparison of results obtained from a conventional mathematical model with those of the ANN-based technique for the case of a 200,000 tdw tanker demonstrates that the approach can successfully predict the vessel’s responses due to arbitrary loads.
Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artiﬁcial Neural Network
: The ﬁnite element method (FEM) is an essential method for predicting the response of the spar platform considering all nonlinear variables. Although FEM is an extremely laborious and time-consuming process for predicting platform responses using hydrodynamic loads, artiﬁcial neural networks (ANNs) can predict the response quickly, as required for platform management to either linger or stop the production of oil and gas. The application of ANN approaches to estimate the wave height and period from the expected wind forces is investigated in this paper. The ANN model can also predict the nonlinear responses of the spar platform subjected to the structural parameter as well as the wave height and wave period. The backpropagation technique depletes feed-forward neural networks, allowing the network to be trained. Following the formation of the neural network, rapid reactions from a freshly anticipated wind force are obtained. The results are validated via a comparison with results from a conventional ﬁnite element analysis. The ﬁndings demonstrate that the artiﬁcial neural network (ANN) technique is effective and is able to signiﬁcantly reduce the required time to make predictions when compared to the conventional FEM.
Copper Phthalocyanine Improving Nonaqueous Catalysis of Pseudomonas cepacia Lipase for Ester Synthesis
The nonaqueous catalysis of lipases is significant for synthesis of high pure esters, but they usually behave low catalytic activity due to denaturation and aggregation of enzyme protein in organic phases. To improve the nonaqueous catalysis, the inexpensive copper phthalocyanine was taken as a new carrier on which Pseudomonas cepacia lipase was immobilized by physical absorption, and used for synthesis of hexyl acetate, an important flavor, via transesterification of hexanol and vinyl acetate. Results showed that the desired loading was 10 mg lipase immobilized on 10 mg copper phthalocyanine powder. When the immobilized lipase was employed in the reaction system consisted of 1.5 mL hexanol and 1.5 mL vinyl acetate at 37℃ and 160 rpm, the conversion was five fold of that catalyzed by native lipase after 1 h, and reached 99.0% after 8 h. Undergoing six times of 8-h reuses, the immobilized lipase had an activity attenuation rate 1.22% h− 1, lower than 1.77% h− 1 of native lipase, which meant that the immobilized lipase was more stable. Even at the room temperature and the static state without shaking or stirring, the immobilized lipase could bring conversion 42.8% after 10 h and the native lipase gave 20.1%. Obviously, the immobilized lipase is an available biocatalyst in organic phase and has great potential in food industry.