A smart non-contact sensor has been developed to better measure, in real time, the acidity of highly radioactive solutions.
This could provide a more efficient and safer mechanism for dealing with legacy waste and recycling spent nuclear fuel without opening, sampling or otherwise manipulating the substance.
There is growing interest in finding a way to better determine the pH level and chemical structure of dissolved nuclear fuel after it has been burned in a reactor. This is because as fuel is reprocessed to separate reusable combustible materials from radioactive waste, variables such as alkalinity and other chemical implications can interfere with how the materials can be recovered. and recycled.
In the first of two steps, researchers at the Pacific Northwest National Laboratory (PNNL), USA, used Raman spectroscopy to create a remote pH sensing technique to measure the interaction of light with bonds chemical or optical monitoring of visible light spectra.
The advantage of Raman probes, scientists say, is that they are commercially available, physically robust and can operate for long periods in harsh environments.
In the second step, a machine learning technique, known as `chemometricsʼwas used to create an algorithm to transform the spectral response into a measure of acidity.
The probes, explains Amanda Lines of the PNNL research team, could be deployed for contact (immersion in a solution or process line) or non-contact (probe placed against a window in the solution or process line) applications. process).
Taking phosphoric acid as a demonstration species because it can “capture the complexity that can be anticipated in a real-world process”, the team built the algorithms to enable automated and accurate conversion of spectral data into information.
“In a complex chemical system like the phosphoric acid solutions described here, accurate data analysis can be extremely difficult,” Lines describes.
“To help us overcome this, we use chemometric analysis. This involves collecting an optical library, or training set, of all potential spectral fingerprints of the chemical species of interest. We then use it to train chemometric models capable of calculating concentration or pH from observed spectral data.
The team incorporated math to compensate for the acid/base pair ratios and activity coefficients of the phosphoric acid solution.
Under different levels of acidity, phosphate can take four chemical forms based on the elimination of protons. The PNNL technique quantifies each type of phosphate and total phosphate, at any pH.
“Our probes provide real-time information, which means that product quantity and material accounting can be immediately corrected or processed as needed, again providing cost savings to the operation,” Lines suggests.
Typically, in the nuclear industry, workers manually take a sample to characterize the chemical composition or measure the pH. The problem is that nuclear fuel recycling or waste treatment is a dynamic process, so this manual sampling can only offer limited or periodic insight.
“This sample manipulation can pose safety risks to workers and is slow, meaning it can take hours to months to measure and understand what is going on in the complex process,” Lines explains.
According to the PNNL, scaling up the results for industrial applications is quite simple. “The type of probe design used here can be integrated into a variety of vessel sizes or process lines, and the instruments can be optimized to simultaneously monitor multiple locations with the process,” says Lines.
“The biggest challenge is preparing chemometric models to handle the increased turbidity of larger-scale process streams, [but] it is something that we have developed and optimized through other projects.’