https://bxscience.enschool.org/ourpages/auto/2014/9/5/42400025/Example%209.pdf

Applications of artificial neural networks for thermal analysis of heat exchangers – A review

Intensive review of AI applied as estimators in chemical process systems.Provide guidelines to select and design AI-based estimators.Discussed the advantages, limitations and compare each algorithm.Future suggestions and directions of the research. The versatility of Artificial Intelligence (AI) in process systems is not restricted to modelling and control only, but also as estimators to estimate the unmeasured parameters as an alternative to the conventional observers and hardware sensors. These estimators, also known as software sensors have been successfully applied in many chemical process systems such as reactors, distillation columns, and heat exchanger due to their robustness, simple formulation, adaptation capabilities and minimum modelling requirements for the design. However, the various types of AI methods available make it difficult to decide on the most suitable algorithm to be applied for any particular system. Hence, in this paper, we provide a broad literature survey of several AI algorithms implemented as estimators in chemical systems together with their advantages, limitations, practical implications and comparisons between one another to guide researchers in selecting and designing the AI-based estimators. Future research suggestions and directions in improvising and extending the usage of these estimators in various chemical operating units are also presented.

Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey

The performance of a solar collector system using sodium carbonate decahydrate (Na"2CO"3.10H"2O) as Phase Change Material (PCM) was experimentally investigated during March and collector efficiency was compared with those of convectional system including no PCM. We also made a series of predictions by using three different soft computing techniques as Artificial Neural Networks (ANN), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Support Vector Machines (SVM). It was found that the solar collector system with PCM is more effective than convectional systems. Soft computing techniques can be used to model of a solar collector with PCM. Furthermore, analysis of soft computing showed that SVM technique gives the best results than that of ANFIS and ANN.

Forecasting of thermal energy storage performance of Phase Change Material in a solar collector using soft computing techniques

Applications of ANNs in flow and heat transfer problems in nuclear engineering: A review work

Prediction of flow fields and temperature distributions due to natural convection in a triangular enclosure using Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN)

Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. Applications of artificial neural network

An artificial neural network (ANN) was applied successfully to predict flow boiling curves. The databases used in the analysis are from the 1960's, including 1,305 data points which cover these parameter ranges: pressure P=100–1,000 kPa, mass flow rate G=40–500 kg/m2-s, inlet subcooling ΔTsub =0–35°C, wall superheat ΔTw = 10–300°C and heat flux Q=20–8,000kW/m2. The proposed methodology allows us to achieve accurate results, thus it is suitable for the processing of the boiling curve data. The effects of the main parameters on flow boiling curves were analyzed using the ANN. The heat flux increases with increasing inlet subcooling for all heat transfer modes. Mass flow rate has no significant effects on nucleate boiling curves. The transition boiling and film boiling heat fluxes will increase with an increase in the mass flow rate. Pressure plays a predominant role and improves heat transfer in all boiling regions except the film boiling region. There are slight differences between the steady and the trans...

Mark Pidduck

Papers

Analysis of the critical heat flux in round vertical tubes under low pressure and flow oscillation conditions. Applications of artificial neural network

Applications of Artificial Neural Network for the Prediction of Flow Boiling Curves