Rivertrace, a market leader in Oil in Water Quality Monitoring, specialises in the design of equipment and systems that ensure strict adherence to environmental regulations in the marine, offshore, and industrial sectors.
All ships, particularly container vessels, need to discharge water primarily for managing ballast, which is crucial for the ship’s stability, manoeuvrability, and structural integrity. Before discharging this water back into the ocean, it’s crucial to ensure that it does not contain oil or other contaminants.
Given the potential ecological impact, the discharge of ballast water is strictly regulated globally, mandating that the water must contain less than 15 parts per million of oil to comply with international standards. Rivertrace has designed a range of Oil Discharge Monitoring Equipment (ODME) to provide means of monitoring, recording and controlling this process.
To ensure accurate measurements of oil content in water, their Smart ODME systems, which rely on an optical sensor, require precise calibration. This process involves several components and steps, focusing primarily on ensuring that the measuring cells (of which there are eight) and the flow rate sensors are calibrated accurately to measure oil content and discharge rate, respectively.
Historically, this was a very time-consuming and manual process, taking on average two days to calibrate a single system. It was our job to streamline this task.
Central to our automated calibration system was the integration of a Kunbus RevPi industrial PC, with additional input/output modules. This setup enables the integration of a programmable injection pump and a proportional valve into the existing system, accompanied by flow and pressure sensors. This allowed us to precisely regulate water flow and oil injection rates, ensuring that the calibration process is both accurate and repeatable.
In addition, the Kunbus unit enables direct communication with the cells, allowing for the acquisition of light sensor readings.
Utilising the data gathered, we could then proceed to generate a calibration curve. This curve enabled us to calculate the concentration of oil in parts per million (ppm) based on the sensor array’s response. The calibration curve essentially translates the light sensors’ readings into a quantifiable measure of oil content, which we can then write back to the cell as a calibration value.
What was once a long and complicated process, requiring highly skilled staff, is now almost completely automated, with only a few simple manual tasks remaining.
As a result, the calibration process, which used to take a day, now only takes 30 minutes and frees up staff to concentrate on other areas of the business. This efficiency gain not only accelerates the process but also allows staff members to redirect their focus towards other strategic aspects of the business.
