Microwave-Assisted Chemical Pre-Treatment of Waste Sorghum Leaves: Process Optimization and Development of an Intelligent Model for Determination of Volatile Compound Fractions

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Pre-Treatment of Waste Sorghum Leaves: Process Optimization and Development of an Intelligent Model for Determination of Volatile Compound Fractions</title> <meta name="description" content="Microwave-Assisted Chemical Pre-Treatment of Waste Sorghum Leaves: Process Optimization and Development of an Intelligent Model for Determination of Volatile Compound Fractions"> <meta name="keywords" content="artificial neural networks, fermentation inhibitors, lignocellulosic pre-treatment, sorghum leaves"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link 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However, the use and pre-treatment of lignocellulosic material are inundated with the generation of fermentation inhibitors which severely impact the feasibility of bioprocesses. This study reports the profiling of all volatile compounds generated during microwave assisted chemical pre-treatment of sorghum leaves. Furthermore, the optimization of reducing sugar (RS) from microwave assisted acid pre-treatment of sorghum leaves was assessed and gave a coefficient of determination (R2) of 0.76, producing an optimal RS yield of 2.74 g FS/g substrate. The development of an intelligent model to predict volatile compound fractions gave R2 values of up to 0.93 for 21 volatile compounds. Sensitivity analysis revealed that furfural and phenol exhibited high sensitivity to acid concentration, alkali concentration and S:L ratio, while phenol showed high sensitivity to microwave duration and intensity as well. These findings illustrate the potential of using an intelligent model to predict the volatile compound fraction profile of compounds generated during pre-treatment of sorghum leaves in order to establish a more robust and efficient pre-treatment regime for biofuel production. <iframe src="https://publications.waset.org/abstracts/56088.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=artificial%20neural%20networks" title="artificial neural networks">artificial neural networks</a>, <a href="https://publications.waset.org/abstracts/search?q=fermentation%20inhibitors" title=" fermentation inhibitors"> fermentation inhibitors</a>, <a href="https://publications.waset.org/abstracts/search?q=lignocellulosic%20pre-treatment" title=" lignocellulosic pre-treatment"> lignocellulosic pre-treatment</a>, <a href="https://publications.waset.org/abstracts/search?q=sorghum%20leaves" title=" sorghum leaves"> sorghum leaves</a> </p> <a href="https://publications.waset.org/abstracts/56088/microwave-assisted-chemical-pre-treatment-of-waste-sorghum-leaves-process-optimization-and-development-of-an-intelligent-model-for-determination-of-volatile-compound-fractions" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56088.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">247</span> </span> </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" 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Microwave-Assisted Chemical Pre-Treatment of Waste Sorghum Leaves: Process Optimization and Development of an Intelligent Model for Determination of Volatile Compound Fractions