One of the greatest challenges to the olive-oil sector is the optimization of the oil production and extraction process, given the large number of factors that must be integrated and the many variables that can affect production, such as paste fineness, mixing time and temperature. Currently, operational control of extraction equipment is generally carried out manually by an experienced operator, posing inherent problems related to subjectivity of human judgment. The standard method to control oil extraction efficiency is the measurement of oil content and moisture in the by-product using chemical extraction, which is usually performed by a laboratory external to the olive oil mill. Given that laboratory data, essential for the optimization of extraction, is obtained long after sampling, there is no access to real-time information on extraction efficiency. Consequently, the process cannot be instantly optimised. As such, SMEs consortium partners have identified a clear need to develop a real-time measurement and automatic control of critical parameters throughout the entire production process as there are currently no automatic control systems for olive-oil mills in the market. The commercial objective of the project is to develop an automatic control system to optimise the oil extraction process in terms of oil yield and working hours, and reduce the pollution levels of the resulting wastewater. Near Infrared technology and predictive control models will be researched for the improvement and maximisation of olive oil extraction. A new and specific NIR sensor, fulfilling the requirements of the olive oil industry, will be developed for on-line measurement of oil and moisture content and an automatic control system with software based on predictive control models will be developed to process sensor data and automatically optimise the olive oil extraction process.
Fields of science
Call for proposal
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Funding SchemeBSG-SME - Research for SMEs
N/A Mosta Mst05