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    <id>6486</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue>6</issue>
    <volume>10</volume>
    <articleNumber>213</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-12-08</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Bio-Methanization of Sheep Manure and Beet Waste in the Meknes–Fès Region, Morocco: Effects of Pretreatment and Machine Learning Applications for Biochemical Methane Potential Prediction</title>
    <abstract language="eng">Sheep manure and beet waste (the uneatable leaf part of the beet) are promising feedstock for biogas production due to their abundance and organic richness. However, their high lignocellulosic content reduces anaerobic digestibility and controls methane yield. This study investigates the effect of various pretreatment strategies, namely physical, thermal, and combined physical–thermal methods, on the Biochemical Methane Potential (BMP) of sheep manure and beet waste. Batch anaerobic digestion experiments were conducted under mesophilic conditions, with BMP values recorded for each treatment. The highest BMP for sheep manure, 125 Nml CH4/g VS, was achieved using combined physical and thermal pretreatment. This approach enhanced methane production by 16%, 25%, and 60% compared to physical pretreatment (PP) alone, thermal pretreatment (TP) alone, and no pretreatment, respectively, while the one BMP for beet waste is 80 Nml CH4/g VS and obtained with thermal pretreatment. To predict BMP outcomes, three machine learning approaches are applied, namely Linear Regression (LM), Random Forest Regression (RFR), and Gradient Boosting Machine (GBM), using digestion time (N days), total solids (Ts), volatile solids (Vs), pretreatment type, and biomass type. The variance analysis confirmed that the interaction between pretreatment and biomass type significantly improved model performance. While diagnostic checks revealed non-linear patterns limiting the linear model, ensemble methods achieved stronger results. The RFR model explained 79.5% of the variance with a Root Mean Square Error (RMSE) of about 15.7, whereas the GBM model achieved the lowest RMSE of 5.05. GBM captures complex non-linear interactions. In addition, variable importance analyses identified digestion time, solid content, and pretreatment as the most influential factors for methane yield, with the combined chemical and physical pretreatment producing the highest biogas outputs. These findings underscore the potential of advanced machine learning models, particularly GBM (Gradient Boosting Machine), for optimizing anaerobic digestion strategies and maximizing biogas recovery from sheep manure and beet waste.</abstract>
    <parentTitle language="eng">Recycling</parentTitle>
    <identifier type="issn">2313-4321</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-64862</identifier>
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    <author>
      <first_name>Meryem</first_name>
      <last_name>Rouegui</last_name>
    </author>
    <author>
      <first_name>Hind</first_name>
      <last_name>Bellabair</last_name>
    </author>
    <author>
      <first_name>Abdelghani</first_name>
      <last_name>El Asli</last_name>
    </author>
    <author>
      <first_name>Amine</first_name>
      <last_name>Amar</last_name>
    </author>
    <author>
      <first_name>Wilfried</first_name>
      <last_name>Zörner</last_name>
    </author>
    <author>
      <first_name>Fouad</first_name>
      <last_name>Rachidi</last_name>
    </author>
    <author>
      <first_name>Rachid</first_name>
      <last_name>Lghoul</last_name>
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    <collection role="institutes" number="15985">Fakultät Maschinenbau</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="persons" number="26227">Zörner, Wilfried</collection>
    <collection role="institutes" number="19321">Institut für neue Energie-Systeme (InES)</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6486/recycling-10-00213.pdf</file>
  </doc>
  <doc>
    <id>5552</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>29207</pageFirst>
    <pageLast>29223</pageLast>
    <pageNumber/>
    <edition/>
    <issue>22</issue>
    <volume>15</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
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    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Assessment of agricultural residues for small-scale biogas plants and adoption drivers: a case study of the Fès-Meknès region in Morocco</title>
    <abstract language="eng">Small-scale biogas systems hold promise as reliable renewable energy sources in developing nations; however, adequate and consistent supply of feedstock remains a challenge. Agricultural residue, due to their lack of competition with food crops for resources, is touted as a dependable feedstock choice. This article therefore examines agricultural residues as potential biogas plant feedstocks in the Fès-Meknès region of Morocco, using a structured farm survey to evaluate livestock types, crop varieties, and residue utilization. Additionally, the study explores the challenges and drivers influencing biogas technology adoption in Morocco. Findings indicate a predominance of small-scale farms with livestock (averaging 11 cattle, 45 sheep, and 20 chicken) and mainly subsistence crop production, making these farms suitable candidates for small-scale biogas plants. Key barriers to adoption include a lack of awareness about the technology, along with technical and financial constraints. However, raising awareness, establishing demonstration plants, and offering financial and non-financial incentives are identified as potential drivers of adoption. This research provides a foundation for implementing biogas technologies in the case study area and other developing nations, guiding researchers and governmental and non-governmental organizations in disseminating small-scale biogas systems as a reliable energy source and a method for converting agricultural residues into sustainable energy (biogas) and fertilizer.&#13;
 Graphical Abstract</abstract>
    <parentTitle language="eng">Biomass Conversion and Biorefinery</parentTitle>
    <identifier type="issn">2190-6815</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-55523</identifier>
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    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Joshua Kiplagat</first_name>
      <last_name>Ngetuny</last_name>
    </author>
    <author>
      <first_name>Jalal</first_name>
      <last_name>Hsaine</last_name>
    </author>
    <author>
      <first_name>Abdelaalim</first_name>
      <last_name>Mabrouki</last_name>
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    <author>
      <first_name>Fouad</first_name>
      <last_name>Rachidi</last_name>
    </author>
    <author>
      <first_name>Abdelghani</first_name>
      <last_name>El Asli</last_name>
    </author>
    <author>
      <first_name>Wilfried</first_name>
      <last_name>Zörner</last_name>
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    <collection role="institutes" number="15985">Fakultät Maschinenbau</collection>
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    <collection role="persons" number="26227">Zörner, Wilfried</collection>
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    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
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