[HTML][HTML] Dynamic modeling and control strategies of organic Rankine cycle systems: Methods and challenges

M Imran, R Pili, M Usman, F Haglind - Applied energy, 2020 - Elsevier
Organic Rankine cycle systems are suitable technologies for utilization of low/medium-
temperature heat sources, especially for small-scale systems. Waste heat from engines in …

Application of novel framework based on ensemble boosted regression trees and Gaussian process regression in modelling thermal performance of small-scale …

Z Said, P Sharma, AK Tiwari, Z Huang, VG Bui… - Journal of Cleaner …, 2022 - Elsevier
This work examined the thermal performance of a small-scale solar organic Rankine cycle
system, in which a flat plate solar collector was employed to supply heat to the organic …

[HTML][HTML] Overview on artificial intelligence in design of Organic Rankine Cycle

D Zhao, S Deng, L Zhao, W Xu, W Wang, X Nie… - Energy and AI, 2020 - Elsevier
Converting thermal energy into mechanical work by means of Organic Rankine Cycle is a
validated technology to exploit low-grade waste heat. The typical design process of Organic …

An LSTM short-term solar irradiance forecasting under complicated weather conditions

Y Yu, J Cao, J Zhu - IEEE Access, 2019 - ieeexplore.ieee.org
Complicated weather conditions lead to intermittent, random and volatility in photovoltaic
(PV) systems, which makes PV predictions difficult. A recurrent neural network (RNN) is …

Thermophysical profile of graphene oxide and MXene hybrid nanofluids for sustainable energy applications: Model prediction with a Bayesian optimized neural …

PK Kanti, P Sharma, B Koneru, P Banerjee, KD Jayan - FlatChem, 2023 - Elsevier
Nanofluids (NFs) as heat transfer fluids (HTFs) have enormous promise in heat exchange
systems. One of the key problems for the use of NFs is how to balance effective thermal …

Performance prediction of a cryogenic organic Rankine cycle based on back propagation neural network optimized by genetic algorithm

Z Tian, W Gan, X Zou, Y Zhang, W Gao - Energy, 2022 - Elsevier
In this paper, a performance prediction model of the cryogenic ORC was presented based
on the back propagation neural network optimized by the genetic algorithm (BPNN-GA) …

An efficient multilayer adaptive self-organizing modeling methodology for improving the generalization ability of organic Rankine cycle (ORC) data-driven model

X Ping, F Yang, H Zhang, C Xing, A Yang… - … Applications of Artificial …, 2023 - Elsevier
The efficient and accurate model construction of an organic Rankine cycle (ORC) system is
the key to its analysis, prediction, and optimization. As a typical multidisturbance nonlinear …

Detection of COVID-19 chest X-ray using support vector machine and convolutional neural network

DCR Novitasari, R Hendradi, RE Caraka… - Commun. Math. Biol …, 2020 - scik.org
This study aims to detect whether patients examined are healthy, Coronavirus positive, or
just have pneumonia based on chest X-ray data using Convolutional Neural Network …

Applying artificial neural network to approximate and predict the transient dynamic behavior of CO2 combined cooling and power cycle

J He, L Shi, H Tian, X Wang, X Sun, M Zhang, Y Yao… - Energy, 2023 - Elsevier
The CO 2 combined cooling and power cycle (CCP) is a promising alternative for waste heat
recovery due to its environmental friendliness and excellent performance. However, the …

Machine learning prediction of ORC performance based on properties of working fluid

Y Peng, X Lin, J Liu, W Su, N Zhou - Applied Thermal Engineering, 2021 - Elsevier
In order to develop machine learning methods for performance prediction of basic ORC
(BORC) and regenerative ORC (RORC), thermodynamic properties of working fluids are …