Duckweed has emerged as a potential feedstock for the environmentally sustainable and economically viable production of biofuels and protein. The aim of this study was to: (1) enhance an existing intrinsic duckweed growth model and use it to develop a general regression model that enables users to easily predict annual duckweed yield for large scale applications; and (2) determine the optimal parameter sets that produce the highest annual duckweed yield at a specific location for known values of daily temperature and photoperiod. Simulations performed using Stella Architect were used to compute annual duckweed yield and generate separate datasets for developing the regression model and optimization model. To improve duckweed yields for large-scale applications which incorporate regular harvesting, a harvesting regime was added to the intrinsic duckweed growth model. Two new parameters (harvest frequency and harvest ratio) and a control (harvest threshold) were used to describe the harvesting regime in the model. The general model was developed by fitting LASSO regression (R2 = 0.95) with four variables: initial mat density, intrinsic growth rate, harvest ratio, and harvest frequency. This model offers a simple method for users to estimate annual duckweed yield in practical applications without the need for dynamic simulation runs. Optimum parameter values to maximize biomass production at a location in southwest Florida, USA, were determined using an optimization framework involving a deep neural network machine learning algorithm. Using an existing daylength model to predict daily photoperiod and inputting local temperature data, machine learning calculated a maximum yield of 70 dry tons per hectare per year for the Florida case study, under the following conditions: initial mat density = 169 gdry m−2; harvest threshold = 76 gdry m−2; nitrogen = 50.1 mg L−1; phosphorus = 7.5 mg L−1; harvest ratio = 0.35; and harvest frequency = 1 day.
All Science Journal Classification (ASJC) codes
- Renewable Energy, Sustainability and the Environment
- Environmental Science(all)
- Strategy and Management
- Industrial and Manufacturing Engineering