DEVELOPMENT OF A NOVEL, COST EFFECTIVE TECHNIQUE TO OPTIMISE OLIVE OIL PRODUCTION

Project Information

OLICEMATIC

Grant agreement ID: 222500

Project closed

Start date 1 September 2008

End date 28 February 2011

Funded under

• Specific Programme "Capacities": Research for the benefit of SMEs

Total cost € 1 165 166,89

EU contribution € 853 958,00

Coordinated by

NTRAM GENERAL S.A.
Spain

Final ReportSummary - OLICEMATIC (Development of a Novel, Cost Effective Technique to Optimise Olive Oil Production)

This project addresses the need of European olive oil production sector for developing and automated control sytem to monitor and control their production process. Due to the variability of the oil extractability and the complex dependence of this parameter on a wide number of variables (olive variety, maturity, climatic conditions, etc), the determination of the optimum process parameters for optimizing oil quality and production yield are difficult to set.

In this project, a network of sensing / actuating elements has been designed and installed at an olive oil production plant. The network also included low cost near infrared (NIR) sensing modules for the determination of fat and water content, which were developed in the framework of the project. The analysis of the process allowed identifying suitable regulation and control actions, which were automated and integrated into the OLICEMATIC system through the use of actuating elements, controlled by supervisory control and data acquisition (SCADA).

Validation results have proved that the monitoring / control system can help olive oil producers to maximise their production yield by providing useful information on different process parameters. In addition, the control system ensures stable and adequate processing conditions, which are particularly important for the production of highest quality (extra virgin) olive oil.

Project context and objectives:

The European Union (EU) is the world leader in the olive oil sector, responsible of 75% of the oil production and 70 % of the consumption world-wide. It is of significant importance to European agriculture, counting with 2.5 million producers, and is the main source of employment and economic activity in many producing regions. Olive farming and oil production is critical for Europe's rural economy and has shaped the landscape in olive oil producing countries over many centuries.

One of the greatest challenges to the olive-oil sector is the optimisation of the oil production and extraction process, given the many variables that can affect the production of quality oils, such as paste fineness, mixing time and temperature, decanter efficiency, among others. In general, the operational control of the extraction equipment is carried out manually by an experienced operator, the mill master, with all the inherent problems associated with the subjective nature of human judgement. Moreover, the standard method to control oil extraction efficiency is the measurement of the oil content in the by-product, using chemical extraction, which in most cases is performed by a laboratory, external to the olive oil mill. The mill master obtains only the laboratory data long after the sampling. Consequently the process cannot be instantly optimised as the mill master does not have real-time information on the extraction efficiency. Alternatively, in smaller mills, judgment calls on oil content are commonly done by 'educated guesses' on the part of the mill master who, for the most part, does not have access to quantitative information on oil-water content of the olive paste.

This project has addressed the need for developing novel automation and control techniques for the olive oil industry. The key to optimisation lies in the real-time measurement and automatic control of critical parameters throughout the entire process. Currently, there are no automatic control systems for olive-oil mills in the market. As well, no real time measurement systems exist for determining a key parameter of the mixing and decantation process: the oil / water content of the olive paste. Furthermore, this project has aimed to cover two gaps in the current state-of-the-art olive-oil extraction process:

(1) the lack of an olive-oil-specific sensor for the measurement of oil / water content (in both mediums, pomace, the crushed or ground, pulpy substance, and the by-product; and
(2) the lack of an automatic control system for the optimisation of the whole oil extraction process, including the developed sensor.

The proposed sensor is based on NIR technology to offer real-time quantification of the water and oil content in olive paste. In addition to these novel fat / water sensors, the control system relies on the use of accurate monitoring sensors and automated actuating elements, which are controlled by a dedicated hardware and software based on predictive control models, and optimisation algorithms.

To attain these ambitious objectives, a multidisciplinary consortium formed by technical companies, end-user small and medium-sized enterprises (SMEs), and universities has been engaged. CRIC, the project coordinator, is a leading company providing research and development (R&D) and engineering services to third parties. DVC Ltd. was founded in 1987, and provides electronic design services to customers in the medical and other industrial sectors. ESDL is a Maltese company experienced in developing control solutions for the industry. AUTELEC S.A. designs, produces and distributes equipments to the food industry, specially, to the olive oil industry. Aceites Malagón SL is a small to medium-sized enterprise (SME) company based in Spain, producing highest quality olive oil. Addato is a second end-user SME, producing olive oil in Italy. Universitat Politècnica de Catalunya is a Spanish university with a huge experience in the development of sensors and electronic systems. Finally, MIIS is a Maltese company designing electronics and control hardware / software for industrial partners.

The objective of this project is to develop and 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. NIR technology and predictive control models (PCM) will be researched for the improvement and maximisation of olive oil extraction.

The system will be implemented for oil mills, which use a two-phase extraction system. By doing so, the competitiveness of olive oil processing mills will increase as follows:

(1) By automatically optimising the extraction processes, olive mills will increase their virgin olive oil output, thus increasing productivity and profitability. It is expected that industrial efficiency increases in the region of 1 % will be achieved.
(2) The optimisation of the process will substantially reduce the residues involved in olive oil production. Controlling and optimising water consumption and oil content in the by-products, and reducing the hazardous levels of the resulting vegetable water, which proves difficult and costly to recycle. This will ensure the sustainability of olive oil processing in Europe, especially in light of the dry climatic conditions of regions typical of olive cultivation.
(3) Users will save in costs related to subcontracting laboratory analyses, as they will be able to carry out in-house analysis and control.

Project results:

During the implementation phase of the project, a low cost NIR sensor was developed. Laboratory tests have proved the sensitivity of the sensing module, in spite of the fact that the performance of the module is presently limited by long term drifts in the NIR signals. In addition to this laboratory work, three NIR modules were produced and integrated at different points of an oil production plant. Specific mechanical and fluidic components were designed in order to allow monitoring the fat content in the process.

In addition to the development and integration of the NIR sensors, a network of sensors was installed also in the plant, in order to obtain basic process data, including processing temperatures, flows, etc. Process data was integrated in a plant model, which allow establishing control and regulation actions, which were automated by means of key actuating elements installed at the plant. The complete system was tested and validated at real industrial scale level, in a 4000 kg/h processing line.

The OLICEMATIC control system allows ensuring the thermal stability of the malaxation process, while provided also useful information of the oil extractability. Through the use of this control system, the quality of the oil produced can be granted, while optimum processing conditions can be tightly set, which result in an overall improvement of the extra virgin oil production yield estimated at around 1 %.

Potential impact:

The potential market for OLICEMATIC system is significant, due to the tremendous economic losses annually suffered by oil producers, as a result of the fact that control actions are carried out by the mill master with limited information on the process.

At the present stage, industrial partners consider that the OLICEMATIC possibly needs further developments aiming at improving its functionality, and reducing its production and installation costs.

Project website:
http://olicematic.cric-projects.com

Contact details:
Estela Pacheco
Centre de Recerca i Innovació de Catalunya
E-mail: [e-mail]
Tel: [phone]