Tummawai Thananop, Rohitatisha Srinophakun Thongchai, Padungthon Surapol, Sukpancharoen Somboon
Department of Mechanical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand.
Department of Chemical Engineering, Faculty of Engineering, Kasetsart University, Bangkok 10900, Thailand.
ACS Omega. 2024 Nov 7;9(46):46017-46029. doi: 10.1021/acsomega.4c05971. eCollection 2024 Nov 19.
By integrating innovative technologies to enhance the efficiency and sustainability of production, this study specifies the establishment of a cutting-edge growing system for sp. microalgae. Improvement of a system for the real-time, noninvasive observation and management of algae growth employing a closed tubular photobioreactor (PBR) engineered with computational fluid dynamics (CFD), combined with the Internet of things (IoT), artificial intelligence (AI), and image processing technologies was the major goal of this research. The fitting of seven types of sensors to identify key characteristics such as temperature, pH, light intensity, electrical conductivity (EC), flow rate, oxygen content, and light exposure duration was included in the research method. To manage the gaining of sensor data and system operations, an ESP8266 microcontroller was used as the main control unit, while 33 × 33 pixel images were taken with an ESP32 camera at 30 min intervals to assess growth by evaluating color intensity, enabling real-time evaluation of algal density without sampling or disturbing growth. Forecasting and enhancing farming situations was the goal of producing these machine learning (ML) models. Uniformly dispersed between 12 and 24 h light cycles, the data set comprised 602 samples. Considerable improvements were observed in the results for biomass productivity, with constant 24 h lighting yielding a 7.19% increase, counter to a 2.09% increase seen in the 12 h cycle. Temperature and light intensity are the most significant parameters for growth, as revealed by analysis of Feature Importance. The eXtreme Gradient Boosting (XGBoost) model showed remarkable effectiveness in terms of projecting growth, attaining an value of 0.9997 for the training data set. With important benefits for the development of renewable energy, food supply, and environmental modification in the future, this research highlights the competence of intelligent technology to strengthen microalgae production.
通过整合创新技术以提高生产效率和可持续性，本研究详细说明了为某特定微藻物种建立一个前沿的生长系统。本研究的主要目标是改进一个利用计算流体动力学（CFD）设计的封闭式管状光生物反应器（PBR），结合物联网（IoT）、人工智能（AI）和图像处理技术，对藻类生长进行实时、非侵入性观察和管理的系统。研究方法包括安装七种类型的传感器，以识别温度、pH值、光照强度、电导率（EC）、流速、氧气含量和光照持续时间等关键特征。为了管理传感器数据的获取和系统操作，使用ESP8266微控制器作为主控制单元，同时使用ESP32相机每隔30分钟拍摄33×33像素的图像，通过评估颜色强度来评估生长情况，从而能够在不采样或不干扰生长的情况下实时评估藻类密度。生成这些机器学习（ML）模型的目的是预测和改善养殖情况。数据集均匀分布在12至24小时的光照周期之间，包含602个样本。在生物质生产力的结果中观察到了显著改善，24小时持续光照使产量提高了7.19%，而在12小时周期中产量提高了2.09%。特征重要性分析表明，温度和光照强度是生长的最重要参数。极端梯度提升（XGBoost）模型在预测生长方面显示出显著效果，训练数据集的 值达到0.9997。这项研究突出了智能技术在加强微藻生产方面的能力，对未来可再生能源发展、食品供应和环境改善具有重要意义。
