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Multi-layer perceptron artificial neural network (MLP-ANN) prediction of biomass higher heating value (HHV) using combined biomass proximate and ultimate analysis data

Modeling Earth Systems and Environment, 2021
Biomass is an important renewable energy source and studies on its properties help determine the suitable choice of process to harness this energy for a specific biomass type. Higher heating value (HHV) is a measure of the biomass energy content, and recent studies have now focused on non-empirical techniques of predicting it. The aim of this study was
Adewale George Adeniyi   +2 more
exaly   +2 more sources

Estimation of biomass higher heating value (HHV) based on the proximate analysis by using iterative neural network-adapted partial least squares (INNPLS)

Energy, 2017
Abstract The higher heating value (HHV) of biomass fuels is a crucial factor in the techno-economic analysis and subsequent development of bioenergy projects. In this study, iterative neural network-adapted partial least squares (INNPLS) was applied to estimate the HHV of biomass fuels as a function of fixed carbon (FC), volatile matters (VM), and ...
Meisam Tabatabaei   +2 more
exaly   +2 more sources

Higher heating value (HHV) of vegetable oils, fats and biodiesels evaluation based on their pure fatty acids' HHV

Energy, 2012
Higher heating value (HHV) is an important property of a fuel, especially for vegetable oils, fats and their derivatives which are intended to replace petroleum fuels. There are lots of methods to evaluate the HHV of a fuel. In this paper, the HHV of vegetable oils, fats and their derivatives (biodiesels) is calculated by using the HHV of their pure ...
exaly   +2 more sources

Biomass higher heating value (HHV) modeling on the basis of proximate analysis using iterative network-based fuzzy partial least squares coupled with principle component analysis (PCA-INFPLS)

Fuel, 2018
Abstract In this study, a novel iterative network-based fuzzy partial least squares coupled with principle component analysis (PCA-INFPLS) was proposed to predict the HHV of biomass fuels as a function of fixed carbon (FC), volatile matter (VM), and ash content.
Meisam Tabatabaei   +2 more
exaly   +2 more sources

Higher Heating Values (HHV) Prediction Model from Biomass Proximate Analysis Data

open access: yes, 2014
The Higher Heating Values (HHV) of biomass is an important parameter in modeling biomass to energy conversion processes. There are several published models and each of them has pros and cons.
Isah Yakub Mohammed   +4 more
core   +8 more sources

Evaluasi Prediksi Higher Heating Value (HHV) Biomassa Berdasarkan Analisis Proksimat

Risalah Fisika, 2020
Abstrak – Biomassa merupakan salah satu energi terbarukan yang sangat mudah ditemui, ramah lingkungan dan cukup ekonomis. Keberadaan biomassa dapat dimaanfaatkan sebagai pengganti bahan bakar fosil, baik itu minyak bumi, gas alam maupun batu bara. Analisi diperlukan sebagai dasar biomassa sebagai energi seperti proksimat dan kalor.
Made Dirgantara   +2 more
openaire   +1 more source

Prediction of Higher Heating Value HHV of Date Palm Biomass Fuel using Artificial Intelligence Method

2019 8th International Conference on Renewable Energy Research and Applications (ICRERA), 2019
Date palm biomass can be considered as an alternative to conventional energy combined with other renewable energy sources in the oasis. Its energy recovery requires a precise knowledge of its energy rate potential represented by its calorific value. Relationships of ultimate and proximate analysis of date palm biomass with higher heating value (HHV ...
Bousdira Khalida   +5 more
openaire   +1 more source

DETERMINATION OF HIGHER HEATING VALUES (HHVS) OF BIOMASS FUELS

2016
Current issues related to fuels can be grouped under four headings. Today world is facing three critical problems: (1) high fuel prices, (2) climatic changes, (3) air pollution, and [1] heating value. Higher Heating Value (HHV) is one of the most important properties of fuels which explain the higher energy content and determine the efficient use of ...
Acar, S., Ayanoglu, A., Demirbas, A.
openaire   +1 more source

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