Overview on artificial intelligence in design of Organic Rankine Cycle

Towards the digitalisation of porous energy materials: evolution of digital approaches for microstructural design

“…Recently, AI has been applied in various scientific and engineering areas including material science due to its abilities in data mining and feature extraction. [168][169][170][171][172][173][174][175] In conjunction with the experimental techniques and mathematical models introduced in Section 3, AI techniques have been successfully employed in the study of energy materials. Various machine learning (ML) methods and advanced deep neural networks (DNN) have shown excellent performance in regard to material structure reconstruction and generation, and property and performance prediction, such as artificial neural networks (ANN), 174 support vector machines (SVM), 176 convolutional neural networks (CNN), [177][178][179][180][181][182] generative adversarial neural networks (GANN) [183][184][185][186][187][188][189][190] and so on.…”

An overview on subcritical organic rankine cycle configurations with pure organic fluids

“…The GA method has been widely applied in the selection of working fluid, optimization of operation parameters, and multiobjective optimization, 118 due to simple operation and strong extensibility. However, the selections of the crossover and mutation operators are highly depended on the experience, and the original population affects the calculation results.…”

Comparison between deep learning and fully connected neural network in performance prediction of power cycles: Taking supercritical CO 2 Brayton cycle as an example

“…With the rapidly increasing influence of big data and cloud computing, artificial intelligence (AI) will play a pivotal role in promoting carbon‐neutral energy revolution 1 . The designing of power cycle is significant in the energy system, but the process is limited by time‐consuming thermodynamic models for calculating power cycle performances, especially when power cycle evolves into more complex due to hybridizing with renewable power source and energy storage system 2 . The more complexities and uncertainties in the future carbon‐neutral energy system will enlarge the calculation scale, thus more efficient calculating methods are needed.…”

“…As a kind of AI, the data‐driven machine learning (ML) method, such as artificial neural network (ANN), can establish precise relationship between two sets of complex variables and provide instant predictions, which is completive for replacing traditional time‐consuming mathematical models in predicting cycle performances, and greatly accelerate the designing process of power cycles 2 . ANN has been widely used for performance prediction in the designing process of power cycles in recent years, including the basic organic Rankine cycle (ORC), 3 ORC with diesel engine, 4 ORC with in‐flow radial turbine, 5 ORC in off‐design condition, 6 and so on.…”

“…1 The designing of power cycle is significant in the energy system, but the process is limited by time-consuming thermodynamic models for calculating power cycle performances, especially when power cycle evolves into more complex due to hybridizing with renewable power source and energy storage system. 2 The more complexities and uncertainties in the future carbon-neutral energy system will enlarge the calculation scale, thus more efficient calculating methods are needed.As a kind of AI, the data-driven machine learning (ML) method, such as artificial neural network (ANN), can establish precise relationship between two sets of complex variables and provide instant predictions, which is completive for replacing traditional time-consuming mathematical models in predicting cycle performances, and greatly accelerate the designing process of power cycles. 2 ANN has been widely used for performance prediction in the designing process of power cycles in recent years, including the basic organic Rankine cycle (ORC), 3 ORC with diesel engine, 4 ORC with in-flow radial turbine, 5 ORC in off-design condition, 6 and so on.…”