Integrating Life Cycle Assessment and Machine Learning to Enhance Black Soldier Fly Larvae-Based Composting of Kitchen Waste

Aug 5, 2026

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Kitchen waste can account for roughly 40%–60% of municipal solid waste, making it a high-value feedstock for compost production. Space- and time-intensive conventional composting (e.g., windrow, vermi-, and bin) motivates alternative biological treatment. This study combines experimental data, life cycle assessment, and machine learning using a Levenberg–Marquardt-trained artificial neural network to optimize black soldier fly larvae (BSFL) composting. Key drivers include treatment time, larval population, aeration frequency, waste composition, and container surface area, and the approach delivers strong reduction and quality improvements.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Abstract

Introduction

Municipal solid waste challenges

Organic fraction and composting pathways

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sustainability Article Integrating Life Cycle Assessment and Machine Learning to Enhance Black Soldier Fly Larvae-Based Composting of Kitchen Waste Muhammad Yousaf Arshad 1, *, Salaha Saeed 1, Ahsan Raza 2, Anum Suhail Ahmad 3, Agnieszka Urbanowska 4, Mateusz Jackowski 5 and Lukasz Niedzwiecki 6,7 Citation: Arshad, M.Y.; Saeed, S.; Raza, A.; Ahmad, A.S.; Urbanowska, A.; Jackowski, M.; Niedzwiecki, L. Integrating Life Cycle Assessment and Machine Learning to Enhance Black Soldier Fly Larvae-Based Composting of Kitchen Waste. Sustainability 2023, 15, 12475 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)su151612475 Academic Editor: Diego Augusto De Jesus Pacheco Received: 30 June 2023 Revised: 6 August 2023 Accepted: 7 August 2023 Published: 16 August 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Corporate Sustainability and Digital Chemical Management, Interloop Limited, Faisalabad 38000, Pakistan; [s.saeed.uet@gmail.com](s.saeed.uet@gmail.com) 2 Aziz Fatimah Medical and Dental College, Faisalabad 38000, Pakistan; [ahsanra33@gmail.com](ahsanra33@gmail.com) 3 Halliburton Worldwide, Houston, TX 77032, USA; [anam.ahmed@halliburton.com](anam.ahmed@halliburton.com) 4 Department of Environment Protection Engineering, Faculty of Environmental Engineering, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland; [agnieszka.urbanowska@pwr.edu.pl](agnieszka.urbanowska@pwr.edu.pl) 5 Department of Micro, Nano and Bioprocess Engineering, Faculty of Chemistry, Wroclaw University of Science and Technology, 50-373 Wroclaw, Poland; [mateusz.jackowski@pwr.edu.pl](mateusz.jackowski@pwr.edu.pl) 6 Department of Energy Conversion Engineering, Wrocław University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wrocław, Poland; [lukasz.niedzwiecki@pwr.edu.pl](lukasz.niedzwiecki@pwr.edu.pl) 7 Energy Research Centre, Centre for Energy and Environmental Technologies, VŠB—Technical University of Ostrava, 17 . Listopadu 2172/15, 708 00 Ostrava, Czech Republic * [Correspondence: yousaf.arshad96@yahoo.com](Correspondence: yousaf.arshad96@yahoo.com) Abstract: Around 40% to 60% of municipal solid waste originates from kitchens, offering a valuable resource for compost production. Traditional composting methods such as windrow, vermi-, and bin composting are space-intensive and time-consuming. Black soldier ﬂy larvae (BSFL) present a promising alternative, requiring less space and offering ease of handling. This research encompasses experimental data collection, life cycle assessment, and machine learning, and employs the Levenberg– Marquardt algorithm in an Artiﬁcial Neural Network, to optimize kitchen waste treatment using BSFL. Factors such as time, larval population, aeration frequency, waste composition, and container surface area were considered. Results showed that BSFL achieved signiﬁcant waste reduction, ranging from 70% to 93% by weight and 65% to 85% by volume under optimal conditions. Key ﬁndings included a 15-day treatment duration, four times per day aeration frequency, 600 larvae per kilogram of waste, layering during feeding, and kitchen waste as the preferred feed. The larvae exhibited a weight gain of 2.2% to 6.5% during composting. Comparing the quality of BSFL compost to that obtained with conventional methods revealed its superiority in terms of waste reduction (50% to 73% more) and compost quality. Life cycle assessment conﬁrmed the sustainability advantages of BSFL. Machine learning achieved high accuracy of prediction reaching 99.5% . Keywords: black soldier ﬂy larvae; kitchen waste; life cycle asse

FAQs

Why are black soldier fly larvae (BSFL) considered an alternative to conventional composting for kitchen waste?

BSFL composting requires less space and is easier to handle, while still enabling efficient treatment of kitchen waste compared with windrow, vermi-, and bin methods.

Which factors were optimized in the BSFL kitchen waste composting model?

The study considers time, larval population, aeration frequency, waste composition, and container surface area to optimize treatment performance.

What results did the optimized BSFL treatment achieve and how was prediction accuracy reported?

Under optimal conditions, BSFL achieved substantial waste reduction (about 70%–93% by weight and 65%–85% by volume), and the machine learning model reached high prediction accuracy up to 99.5%.

Aug 5, 2026

Upload by Eliana