{"title":"Pyrolysis parameter based optimization study using response surface methodology and machine learning for potato stalk","authors":"Ahmad Nawaz , Shaikh Abdur Razzak , Pradeep Kumar","doi":"10.1016/j.jtice.2024.105476","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><p>The depletion of fossil fuel supplies, along with ever-increasing energy needs, mandates the investigation of clean and renewable fuels. In this regard, the present investigation pursued to assess the suitability of response surface methodology (RSM) and machine learning strategy for optimising the process parameters of potato stalk (PS) pyrolysis.</p></div><div><h3>Methods</h3><p>The experiment was performed in a tubular reactor, and key process factors for example temperature (400 – 650°C), heating rate (50 – 100°C/min), and N<sub>2</sub> flow rate (150 – 200 ml/min) were optimised for maximum bio-oil yield. The key features of the produced liquid product (bio-oil) and solid product (biochar) were investigated.</p></div><div><h3>Significant Findings</h3><p>The PS physicochemical study demonstrated enormous bioenergy potential, with higher carbon content (45.82 %), calorific value (17.6 MJ/Kg), and lower moisture content (7.2 wt. %). The coefficient of variation for bio-oil biochar was 1.78 and 1.91 % (less than 10 %), indicating that the model is more reliable and reproducible. The artificial neural network (ANN) better forecasted the process yield; nevertheless, the RSM model successfully forecasted the pyrolysis factors interface and importance. The GCMS analysis of the bio-oil revealed 33.42 % hydrocarbons, 13.42 % esters, 4.62 % acids, 1.71 % ethers, 11.1 % ketones, 14.01 % alcohols, 2.34 % amides, 4.96 % nitrogen-containing substances, and 7.12 % phenols.</p></div>","PeriodicalId":381,"journal":{"name":"Journal of the Taiwan Institute of Chemical Engineers","volume":null,"pages":null},"PeriodicalIF":5.5000,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the Taiwan Institute of Chemical Engineers","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1876107024001342","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Background
The depletion of fossil fuel supplies, along with ever-increasing energy needs, mandates the investigation of clean and renewable fuels. In this regard, the present investigation pursued to assess the suitability of response surface methodology (RSM) and machine learning strategy for optimising the process parameters of potato stalk (PS) pyrolysis.
Methods
The experiment was performed in a tubular reactor, and key process factors for example temperature (400 – 650°C), heating rate (50 – 100°C/min), and N2 flow rate (150 – 200 ml/min) were optimised for maximum bio-oil yield. The key features of the produced liquid product (bio-oil) and solid product (biochar) were investigated.
Significant Findings
The PS physicochemical study demonstrated enormous bioenergy potential, with higher carbon content (45.82 %), calorific value (17.6 MJ/Kg), and lower moisture content (7.2 wt. %). The coefficient of variation for bio-oil biochar was 1.78 and 1.91 % (less than 10 %), indicating that the model is more reliable and reproducible. The artificial neural network (ANN) better forecasted the process yield; nevertheless, the RSM model successfully forecasted the pyrolysis factors interface and importance. The GCMS analysis of the bio-oil revealed 33.42 % hydrocarbons, 13.42 % esters, 4.62 % acids, 1.71 % ethers, 11.1 % ketones, 14.01 % alcohols, 2.34 % amides, 4.96 % nitrogen-containing substances, and 7.12 % phenols.
期刊介绍:
Journal of the Taiwan Institute of Chemical Engineers (formerly known as Journal of the Chinese Institute of Chemical Engineers) publishes original works, from fundamental principles to practical applications, in the broad field of chemical engineering with special focus on three aspects: Chemical and Biomolecular Science and Technology, Energy and Environmental Science and Technology, and Materials Science and Technology. Authors should choose for their manuscript an appropriate aspect section and a few related classifications when submitting to the journal online.