Artificial neural networks model for estimating growth of polyculture microalgae in an open raceway pond. (January 2019)

• Record Type: Journal Article
• Authors: Supriyanto, ; Noguchi, Ryozo; Ahamed, Tofael; Rani, Devitra Saka; Sakurai, Kai; Nasution, Muhammad Ansori; Wibawa, Dhani S.; Demura, Mikihide; Watanabe, Makoto M.
• Is Part Of: Biosystems engineering. Volume 177(2019)
• Page Start: 122
• Page End: 129
• Publication Date: 2019-01
• Subjects: Artificial neural network -- Open raceway pond -- Polyculture microalgae -- Hydraulic retention time; Bioengineering -- Periodicals; Agricultural engineering -- Periodicals; Biological systems -- Periodicals; Génie rural -- Périodiques; Systèmes biologiques -- Périodiques; 631
• DOI: 10.1016/j.biosystemseng.2018.10.002
• Languages: English
• ISSNs: 1537-5110

Abstract

Abstract : Microalgae have potential as biomass energy sources with higher photosynthetic efficiency compared to terrestrial plants. The use of polyculture systems such as native microalgae communities for microalgae cultivation has several advantages, as well as challenges due to indeterminate species composition and growth rate variation between species. This paper presents an artificial neural network (ANN) model to estimate the growth of polyculture microalgae in a semi-continuous open raceway pond (ORP). The model was comprised of a multilayer backpropagation neural network with eight input parameters, one hidden layer, and one output parameter. The model was developed using datasets collected from the cultivation of polyculture microalgae in Minamisoma City, Fukushima Prefecture, Japan. The input parameters are as follows: initial algal concentration, harvesting period (between two and three days after the growth have begun), hydraulic retention time, addition of sodium acetate, average solar radiation (μmole m −2 s −1 ), average temperature ( o C), pH condition, and nitrate ion ( NO 3 − ) concentration. The output variable is the microalgae concentration observed during the cultivation period. The output is represented using a single neuron. The result of the study showed that the designed three-layer ANN achieved a high prediction accuracy (R 2 = 0.93) for all combinations of inputs. Highlights: ANN model was proposed to estimate the growth of polyculture microalgae. Developed using 553 datasets collected from semi-continuous Open Raceway Pond. ANN model was able to estimate the growth of polyculture microalgae. Model achieved R 2 more than 0.9 for all input variation. Key variables to estimate microalgal growth were solar irradiance and temperature.