{"title":"基于物联网的 Arduino 有效设计,用于生物丁醇生产中的生物反应器自动控制和样品采集","authors":"Eakkachai Klaithin;Vissavakawn Matimapa-Kay;Wachira Daosud;Yanisa Laoonguthai","doi":"10.1109/JSEN.2024.3469273","DOIUrl":null,"url":null,"abstract":"A successful automatic control was developed using Arduino and ESP32 microcontrollers to operate a low-cost prototype bioreactor, which resulted in the effective production of biobutanol. The system maintained a temperature of 30.70 °C, an average pH of 6.61, and effectively reduced oxygen levels to 0% v/v within 130 s. The highest cell concentration was \n<inline-formula> <tex-math>$1.26 \\times 10^{{8}}$ </tex-math></inline-formula>\n CFU/mL, and GC-FID analysis showed acetone, butanol, and ethanol (ABE) concentrations of 0.3925, 0.05304, and 1.1184 g/L, respectively. In addition, a sample collector was designed to collect samples with a precision of \n<inline-formula> <tex-math>$10 \\; \\pm \\; 1$ </tex-math></inline-formula>\n mL and a time deviation of \n<inline-formula> <tex-math>$2 \\; \\pm \\; 13$ </tex-math></inline-formula>\n s. In terms of the cloud system, NETPIE was used for device management. It can effectively display real-time conditions and control equipment as required. Google Sheets collected 43 200 data points for each parameter for data analysis and observation. Moreover, the Line application was applied for message notification when a sample was collected. The cost of the automated prototype bioreactor was U.S. \n<inline-formula> <tex-math>${\\$}$ </tex-math></inline-formula>\n1093.61 for a 10-L production volume, and the automated sample was U.S. \n<inline-formula> <tex-math>${\\$}$ </tex-math></inline-formula>\n210.99 for 24 tubes, with a maximum volume capacity of 15 mL. Therefore, both the automated bioreactor and the sample collector system were effective for control and monitoring, contributing to improve the biobutanol production process.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"24 22","pages":"37997-38004"},"PeriodicalIF":4.3000,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Effective IoT-Based Arduino Design for Automated Bioreactor Control and Sample Collection in Biobutanol Production\",\"authors\":\"Eakkachai Klaithin;Vissavakawn Matimapa-Kay;Wachira Daosud;Yanisa Laoonguthai\",\"doi\":\"10.1109/JSEN.2024.3469273\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A successful automatic control was developed using Arduino and ESP32 microcontrollers to operate a low-cost prototype bioreactor, which resulted in the effective production of biobutanol. The system maintained a temperature of 30.70 °C, an average pH of 6.61, and effectively reduced oxygen levels to 0% v/v within 130 s. The highest cell concentration was \\n<inline-formula> <tex-math>$1.26 \\\\times 10^{{8}}$ </tex-math></inline-formula>\\n CFU/mL, and GC-FID analysis showed acetone, butanol, and ethanol (ABE) concentrations of 0.3925, 0.05304, and 1.1184 g/L, respectively. In addition, a sample collector was designed to collect samples with a precision of \\n<inline-formula> <tex-math>$10 \\\\; \\\\pm \\\\; 1$ </tex-math></inline-formula>\\n mL and a time deviation of \\n<inline-formula> <tex-math>$2 \\\\; \\\\pm \\\\; 13$ </tex-math></inline-formula>\\n s. In terms of the cloud system, NETPIE was used for device management. It can effectively display real-time conditions and control equipment as required. Google Sheets collected 43 200 data points for each parameter for data analysis and observation. Moreover, the Line application was applied for message notification when a sample was collected. The cost of the automated prototype bioreactor was U.S. \\n<inline-formula> <tex-math>${\\\\$}$ </tex-math></inline-formula>\\n1093.61 for a 10-L production volume, and the automated sample was U.S. \\n<inline-formula> <tex-math>${\\\\$}$ </tex-math></inline-formula>\\n210.99 for 24 tubes, with a maximum volume capacity of 15 mL. Therefore, both the automated bioreactor and the sample collector system were effective for control and monitoring, contributing to improve the biobutanol production process.\",\"PeriodicalId\":447,\"journal\":{\"name\":\"IEEE Sensors Journal\",\"volume\":\"24 22\",\"pages\":\"37997-38004\"},\"PeriodicalIF\":4.3000,\"publicationDate\":\"2024-10-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Journal\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10706801/\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Journal","FirstCategoryId":"103","ListUrlMain":"https://ieeexplore.ieee.org/document/10706801/","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Effective IoT-Based Arduino Design for Automated Bioreactor Control and Sample Collection in Biobutanol Production
A successful automatic control was developed using Arduino and ESP32 microcontrollers to operate a low-cost prototype bioreactor, which resulted in the effective production of biobutanol. The system maintained a temperature of 30.70 °C, an average pH of 6.61, and effectively reduced oxygen levels to 0% v/v within 130 s. The highest cell concentration was
$1.26 \times 10^{{8}}$
CFU/mL, and GC-FID analysis showed acetone, butanol, and ethanol (ABE) concentrations of 0.3925, 0.05304, and 1.1184 g/L, respectively. In addition, a sample collector was designed to collect samples with a precision of
$10 \; \pm \; 1$
mL and a time deviation of
$2 \; \pm \; 13$
s. In terms of the cloud system, NETPIE was used for device management. It can effectively display real-time conditions and control equipment as required. Google Sheets collected 43 200 data points for each parameter for data analysis and observation. Moreover, the Line application was applied for message notification when a sample was collected. The cost of the automated prototype bioreactor was U.S.
${\$}$
1093.61 for a 10-L production volume, and the automated sample was U.S.
${\$}$
210.99 for 24 tubes, with a maximum volume capacity of 15 mL. Therefore, both the automated bioreactor and the sample collector system were effective for control and monitoring, contributing to improve the biobutanol production process.
期刊介绍:
The fields of interest of the IEEE Sensors Journal are the theory, design , fabrication, manufacturing and applications of devices for sensing and transducing physical, chemical and biological phenomena, with emphasis on the electronics and physics aspect of sensors and integrated sensors-actuators. IEEE Sensors Journal deals with the following:
-Sensor Phenomenology, Modelling, and Evaluation
-Sensor Materials, Processing, and Fabrication
-Chemical and Gas Sensors
-Microfluidics and Biosensors
-Optical Sensors
-Physical Sensors: Temperature, Mechanical, Magnetic, and others
-Acoustic and Ultrasonic Sensors
-Sensor Packaging
-Sensor Networks
-Sensor Applications
-Sensor Systems: Signals, Processing, and Interfaces
-Actuators and Sensor Power Systems
-Sensor Signal Processing for high precision and stability (amplification, filtering, linearization, modulation/demodulation) and under harsh conditions (EMC, radiation, humidity, temperature); energy consumption/harvesting
-Sensor Data Processing (soft computing with sensor data, e.g., pattern recognition, machine learning, evolutionary computation; sensor data fusion, processing of wave e.g., electromagnetic and acoustic; and non-wave, e.g., chemical, gravity, particle, thermal, radiative and non-radiative sensor data, detection, estimation and classification based on sensor data)
-Sensors in Industrial Practice