an official journal of: published by:
an official journal of: published by:
Editor in Chief: RAFFAELLO COSSU

WASTEWATER TREATMENT USING BLACK SOLDIER FLY LARVAE: THEORETICAL FRAMEWORK AND PRELIMINARY TESTING FOR CONTINUOUS REACTOR DESIGN

  • Valentina Grossule - Department of Civil, Architectural and Environmental Engineering (DICEA), University of Padova, Italy

Released under CC BY-NC-ND

Copyright: © 2025 CISA Publisher


Abstract

The use of Black Soldier Fly (BSF) larvae has recently been introduced, through the so-called LarWaR process (LARvae for WAstewater treatment and Resource recovery), as a promising alternative biological treatment for high-organic-content wastewater. LarWaR has proven effective in managing high concentrations of organic substances while recovering high-value resources in the form of larval biomass. This study summarises the state of the art of key process aspects within a theoretical design framework, highlighting the persisting research gaps. It investigates an appropriate treatability test for designing and reliably predicting the performance of a continuous reactor treating artificial wastewater. A series of preliminary batch tests were conducted to determine the expected substrate consumption rates (v_s) - fitted using a Michaelis-Menten-like relationship - and removal efficiencies at different loads (F/L, mgC/larva/day). A continuous test was then perfomed to validate the results from the treatability batch tests. The fitted Michaelis-Menten-like model for v_s values proved to be a reliable tool for reactor design, as the steady-state performance (v_s =0.72 mgC/larva/day; η = 36%) closely matched predictions from the treatability test (v_s =0.86 mgC/larva/day, η = 43%). Additionally, the study explored the maximum effective reactor depth (ℎ) for the LarWaR system through video monitoring of larval behavior in deep liquid substrates. Observations revealed physical limitations restricting larval movement to the first few centimeters (3-4 cm) of the liquid surface, providing crucial insights for optimizing reactor depth. These findings contribute to improving the design methodology and scalability of the LarWaR process.

Keywords


Editorial History

  • Received: 12 Feb 2025
  • Revised: 11 Apr 2025
  • Accepted: 12 May 2025
  • Available online: 04 Jun 2025

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