基于物联网的模糊逻辑油藏上涌决策系统

B. Erfianto, N. Suwastika, Sidik Prabowo
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引用次数: 1

摘要

垂直水流导致水库底部沉积物抬升,导致鱼类大量迅速死亡。沉积物主要是鱼类粪便和饲料残渣,导致水面溶解氧(DO)含量从正常值3-6 mg / L急剧下降到1 mg / L以下,这种垂直水流状态称为水库上升流。淡水水体上升流的发生可以通过地表温度和地表下温度差异、DO水平和pH水平等因子进行预测。如果水面温度与水下温度的温差大于5℃,持续时间超过11小时,就会出现上升流。上升流检测系统基于物联网(IoT)通信,利用模糊逻辑决策系统构建。从温度、DO和pH传感器读取的数据被发送到微控制器设备,并通过Internet网络传递给最终用户。在单片机器件上植入模糊逻辑,得到不上升流、潜在上升流和发生上升流的判定条件。上升流检测系统在油藏和测试环境中进行了测试。从测试结果来看,系统读取数据、处理数据并发送给用户,没有任何数据丢失或损坏。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Decision System for Reservoir Upwelling Using Fuzzy Logic Based on Internet of Things
The lifting of sediments at the bottom of the reservoir caused by vertical currents causes rapid mass mortality of fish. The sediment, which is mostly fish excrement and feed residue, causes the dissolved oxygen (DO) content in the water surface to drop dramatically from the normal value of 3–6 mg / L to below 1 mg / L. This vertical current condition is referred to as upwelling of the reservoir. The occurrence of upwelling in freshwater waters can be predicted from factors of difference in surface temperature and under surface temperatures, DO levels and pH levels. Upwelling will occur if the temperature difference between surface temperature and underwater temperature reaches > 5°C for more than 11 hours. The system for detecting upwelling is built on Internet of Things (IoT) communications by utilizing a fuzzy logic decision system. The reading of data from temperature, DO, and pH sensors is sent to the microcontroller device and delivered to the end user via the Internet network. Fuzzy logic implanted on microcontroller device to get the decision condition is not upwelling, potentially upwelling, and upwelling occurs. Upwelling detection systems are tested in reservoirs and in test environments. From the test results the system successfully read data, process data, and send to users without any data lost or damaged.
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