=Paper=
{{Paper
|id=Vol-2030/HAICTA_2017_paper35
|storemode=property
|title=Design and Implementation of a Monitoring and Control Software Tool to Assess Process and Production Data in an Olive Oil Production Unit
|pdfUrl=https://ceur-ws.org/Vol-2030/HAICTA_2017_paper35.pdf
|volume=Vol-2030
|authors=Eleni Kalogianni,Pavlos Salvaras,Dimitrios Bechtsis,Fotis Stergiopoulos
|dblpUrl=https://dblp.org/rec/conf/haicta/KalogianniSBS17
}}
==Design and Implementation of a Monitoring and Control Software Tool to Assess Process and Production Data in an Olive Oil Production Unit==
https://ceur-ws.org/Vol-2030/HAICTA_2017_paper35.pdf
A Monitoring and Control Software Tool to Assess
Process and Production Data in Olive Oil Production
Units
Eleni P. Kalogianni1, Pavlos Salvaras2, Dimitrios Bechtsis3, Fotis Stergiopoulos4
1
Department of Food Technology, Alexander Technological Educational Institute of
Thessaloniki (ATEITh), Greece, email: elekalo@food.teithe.gr
2
Department of Automation Engineering, Alexander Technological Educational Institute of
Thessaloniki (ATEITh), Greece, e-mail: pavlosthess@hotmail.com
3
Department of Automation Engineering, Alexander Technological Educational Institute of
Thessaloniki (ATEITh), Greece, e-mail: dimbec@autom.teithe.gr
4
Department of Automation Engineering, Alexander Technological Educational Institute of
Thessaloniki (ATEITh), Greece, e-mail: fstergio@autom.teithe.gr
Abstract. Monitoring of production processes in the agricultural and food
sector can provide significant advances in equipment life and product quality
and furthermore improve visibility and trackability in the digital supply chain
context. On the other hand, automation and control are of major importance for
flexible and sustainable production systems that minimize labor costs reduce
down time and operation and maintenance (O&M) costs. In this context, an
efficient Monitoring and Control software Tool (MCT) for assessing the
operation data of an olive oil production facility is proposed. The design
architecture of the system is presented followed by practical implementation
data obtained at a small industrial scale olive oil producing facility. Initial
results and information obtained constitute a solid basis on which to found a
future full-scale application of the proposed tool to monitor all stages of the
production facility.
Keywords: monitoring and control, process and production data, olive oil
production.
1 Introduction
Olive oil constitutes a major product in the area of the Mediterranean and a key
ingredient of the diet of the region. Furthermore, its production is a major source of
income for thousands of farmers in a significant number of small to medium scale
production facilities. It is therefore imperative to perform research on how to
optimize both the olive cultivation (Orellana et al., 2011) and the production process
of olive oil as this will have a direct effect on the quality of the final product
(Kalogianni, 2015; Ortega et al., 2016; ), on waste and energy management and thus
on the economy of the process and the competitiveness of the associated businesses.
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Modern Information and Communication Technologies (ICT) can certainly play a
key role in the pursuit of enhanced product quality in the food sector. In particular,
monitoring and control systems and tools can improve visibility and trackability,
creating a digital supply chain in a production process, thus help assess key data and
plan interventions in the production facility in order to optimize the performance and
efficiency of the production cycle and the quality of the final product. For example,
Jimenez et al. (2008) propose the development of a dedicated system based on an
artificial neural network for real-time prediction of the moisture and fat content in
olive pomace using two-phase olive oil processing. A similar approach of using a
method based on Neural Networks (NN) is also presented in Jiménez et al. (2008)
and Jiménez et al. (2009) in an effort to optimize the operation of the Horizontal
Centrifugal Decanter (HCD) in the olive oil production process. Esposto et al. (2009)
propose the use of a specialized analytical system (EOS – Electronic Olfactory
System) with a total of six (6) metal oxide sensors (MOS) installed only in the
malaxer. A similar system (EOS) with six (6) MOS has been also used by Lerma-
García et al. (2009) for the monitoring of the oxidation level of the Virgin Olive Oil
(VOO). Escuderos et al. (2013) propose the use of 14 tin dioxide sensors (SnO2) used
for the analysis of olive oil production, however, at the expense of complexity and
use of custom made sensor technology
The current paper presents the design and pilot test application of a Monitoring
and Control software Tool (MCT) in a small industrial-scale olive oil production
plant used for research and educational purposes located at the Alexander
Technological Educational Institute of Thessaloniki (ATEITh). Compared to the
work presented above, the proposed system is characterized by its simplicity using
widely available sensors, and the use of common industrial protocols, its flexibility in
operation and its ability to present real-time information in what’s most important,
a user-friendly interface. Initial results are encouraging about the potential use of
the tool to obtain and visualize important information to assist in the assessment of
key data in the production process.
The paper is organized as follows: first the system layout and architecture is
presented with details of its characteristics, followed by a description of the pilot test
facility. Then, the developed software and its use in the test rig facility is presented
with a description of the scaling up procedure of the application of the tool to include
all key parameters in a small scale olive oil production unit.
2 System architecture
The basic principle of the system architecture is to have a modular structure with
discrete layers that can be easily integrated into a seamless application. The overall
system architecture includes four (4) layers:
• A layer including embedded sensors of various types located at various
points along the production line
• A data aggregation layer using a standard microcontroller or a
programmable logic controller (PLC)
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• A data manipulation layer using an industrial computer where the web
server components and the applications are installed
• An on-line data monitoring and visualization layer, with (i.e. remotely) or
without (i.e. locally) a web based interface.
The basic system architecture is presented below:
Fig. 1. MCT system architecture
Embedded production sensors provide raw data to the microcontroller/PLC with
the use of external hardware interfaces and dedicated firmware software. The
microcontroller handles raw data by using specific sampling rates and finally
supports bidirectional wireless or wired data communication with the mainframe
computer.
The MCT recognizes the available sensors for each recording, schedules
recordings at specific sampling rates, provides data visualization screens for all
sensors (real-time and historical values of the sensor), identifies out of range values
and provides appropriate feedback to the system and the user and finally saves the
recording in the popular XML (eXtensible Markup Language) format. Moreover the
proposed tool can compare specific sensors from historical data and export historical
recordings to the popular comma separated values (CSV) format for promoting
interoperability with third party software. The tool is developed in the C#
programming language.
3 The test rig facility
Experiments were performed using a small industrial-scale (nominal capacity 500
kgolives/hr) olive oil production unit (Alfa Oliver 500, Alfa Laval) located at ATEITh
(Figure 2). The scale of the plant allowed taking into account process variables and
their variations in an actual production scale. The unit consists of olives washing and
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leaves removal machine, a toothed disc crusher combined with a pit removal system
(operated optionally), 2 malaxers with capability of simultaneous operation under
different conditions (temperature, modified atmosphere, open/closed, paste dilution
rate) a two-phase decanter and a disk centrifuge both with capability of regulating
process temperature and water dilution rate. The processing plant is completed by
two temperature regulated water tanks one for dilution at different process stages and
the other for temperature regulation of the malaxers.
Fig. 2. Olive oil production facility at ATEITh
The operation of the production facility is controlled via a panel located in the
central part of the unit. It is important to note that the user interface of the application
is rather minimal, allowing only basic information to be viewed in small displays, a
fact that necessitates the presence of an operator to record key data about the
production process. Therefore, no analytical data, sampled and manipulated at a
frequent and regular basis can be obtained, nor is it possible to keep a detailed
historical track, to facilitate the assessment of the production process and most
importantly, its effect on the quality of the final product.
The production facility has a control panel with four TLK-31 microcontrollers that
communicate through the RS-485 shield (Figure 3) using the MODBUS - RTU
industrial protocol (Kunte and Shaikh, 2015).
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Fig. 3. MODBUS - RTU Industrial Protocol.
The temperature sensors and the flow sensors provide data via a standard
microcontroller, in this case an Arduino Mega, to an off-line computer which
processes the data and acts as a server. The Arduino Mega is connected to the Server
using the APC 220 (418-455Mhz) wireless radio communication antennas (Figure 4).
Fig. 4 APC 220 wireless radio communication antennas.
4 Developed Software
The initial pilot case application included the incorporation of four PT-100 sensors
connected to the TLK-31 microcontrollers. Two of the sensors were located in the
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malaxers and the other two in the water tanks. The system has also the ability to
connect to up to four flow sensors (G1/2'' OF-201, 1-30 L/min, 3-12V DC, Accuracy:
+/- 0.5%, max pressure: 0.5 MPa at 20 oC) for measuring water or oil quantities. All
sensors have been calibrated before connecting them to the Arduino Mega
(ATmega1280 microcontroller). Suitable, user-friendly interfacing has been
developed and the user can monitor and store information in a compact and efficient
manner. At a first level, the number of sensors, sample rates and scheduling options
can be easily adjusted, as seen in the Figure 5:
Fig. 5. Recording parameters
Excess limit information can be easily captured (textbox with the red background
color in Figure 6). Different visualization patterns (e.g. lines, bars etc.) can be used,
as seen in Figure 6:
Fig. 6. Data visualization options
The user has also the option to save the current session and then view/compare
sensor information from multiple recordings to assist the assessment procedure. Last
but not least, the user can export a recording session in a popular format like
Microsoft Excel for easier manipulation, as seen in Figure 7:
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Fig. 7. Data export capabilities
Finally, the server runs the Apache Web Server and the MySQL database in order to
publish data to the remote users. Users can log in to the web interface tool, perform
real time monitoring of the olive oil unit operations and analyze past recordings
(Figure 8).
Fig. 8. Web-Based Monitoring tool.
5 Scalability potential
From the analysis presented above, it is clear that the proposed MCT is a valuable
tool for the operator to obtain a thorough and clear picture of key data about the
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production process that enables the assessment of the operation, the identification of
points where key adjustments can be made furthermore recorded data can be used to
correlate final product quality with key process variables.
Further work is underway to expand the potential suite of measurements and
provide a scalability potential of the method. Important issues that are currently
under investigation include:
• To increase of number and type of sensors (e.g. flow, pressure, etc.) in order
to measure and control process variables and in order to perform in-line rapid
measurements of olive oil quality
• To obtain a tool that enables in-depth research of process variables on olive
oil quality
• To obtain a critical mass of recordings that will allow useful correlations
between the quality of the final product and key variables in the production
process.
• To enhance the applicability of the method by introducing a fully functional
web-based approach where users can remotely manage olive oil facility.
• Το develop suitable educational material and enrich the educational process
at ATEITh both for students of the agricultural/food sector and the
IT/automation sector, based on the application of the tool.
6 Conclusions
In the previous section the design and development of a Monitoring and Control
Software Tool for enabling the better assessment of key data in a production line has
been presented. Initial tests have been performed for an olive oil production unit
facility located at ATEITh using real time measurements from a set of temperature
sensors. These tests showed the capability of the system to display in a user-friendly
manner and record valuable process data (temperature and flow rate for the moment)
which can be further used for valuable correlations between process variables and
quality as well as an input for process control. The preliminary results obtained show
the great potential of the tool to assist in the assessment and better operation in
general of the production process. Further work is underway to scale up the
application up to its full potential.
Acknowledgments. The development of the pilot system is part of several on-going
final year projects supervised by the Department of Automation Engineering and the
Department of Food Technology of the Alexander Technological Educational
Institute of Thessaloniki (A.T.E.I.Th.). We would like to thank the undergraduate
students Mr. Ioannis Menexes, Mr. Dimitrios Katikaridis, Mr. Athanasios Manolios
and Mr. Giorgos Kontovos, for their contribution to the ongoing development
process.
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