Measuring Algae Growth with Low-Cost Raspberry Pi Sensor System Skip to main content
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2022 Abstracts

Measuring Algae Growth with Low-Cost Raspberry Pi Sensor System

Presenter: Abiela Meek
Authors: Abiela Meek
Faculty Advisor: Ronald Sims
Institution: Utah State University

Around the world the effects of climate change, pollutants, and industry have put pressure on both manmade and natural water systems, as demonstrated through increases in eutrophication of lakes and rivers, causing large and sometimes dangerous algal blooms. The major causes of these blooms are nitrogen and phosphorus, which can come from fertilizers, industrial effluent, and other organic wastes. In response to this, many states and federal agencies instituted or are looking to institute new regulations on allowed phosphorus and nitrogen in treated water to prevent future eutrophication. To attempt to solve these problems, the goal of the Sustainable Waste to Bioproducts Engineering Center is to utilize algae to treat nutrient-rich wastewater to remove nitrogen and phosphorus before it goes back into the environment. In doing this, algal blooms are prevented, and algae can be harvested and used to produce carbon-neutral biofuels, bioplastics, pharmaceuticals, and cosmetic additives – potentially helping to offset the cost of nitrogen and phosphorus removal. A key component of this research is data collection, which can often be expensive and difficult to scale up in the different environments of labs and outdoor treatment areas. As such, this research targets low-cost, effective methods for monitoring bioreactors and finding conditions to increase productivity and reduce harvesting times. To create this system, low-cost sensors and a Raspberry Pi were used to create an easily accessible data collection and storage system. This instrumentation is being used to monitor growing conditions for 81 Rotating Algae Biofilm Reactors and may be expanded to other experiments in the future. This work is expected to provide data that improves knowledge about algae growth patterns, increases productivity, and decreases harvesting times for algae wastewater treatment. If proven effective, the data collected using this system may help revolutionize how wastewater is treated.