Predictive Maintenance BIOMASA FORESTAL

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1 Predictive Maintenance BIOMASA FORESTAL

2 BIOMASA FORESTAL Galician company located in As Pontes, dedicated to manufacture and sale of pellets. Production capacity: T/year. High quality product: Pellet Spanish manufacturer with more quality and environmental sustainability certifications. Commitment to the promotion of the circular economy in our Community: supplying raw materials and manufacturing 100% in Galicia Strategy focused on innovation and continuous process improvement. Proof of this are the collaborations with agents of the innovative ecosystem for the development of R + D + i projects.

3 PRODUCTIVE PROCESS DEBARKING SPLINTING Due to the friction between the trunks, the bark is released and the wood passes clean to the next process. The trunks are reduced to 100 x 100 mm wood chips.

4 PRODUCTIVE PROCESS RAW MATERIAL GRIDING The chip obtained in the previous line goes through the mill to reduce the particle size again to 80 x 5 mm. At this step the humidity is between 45 55%

5 PRODUCTIVE PROCESS DRYING LINE There is an exchange of heat between the hot gases coming from the boiler and the raw material itself. As a result, the raw material reduces its moisture content from 50% to 10%.

6 PRODUCTIVE PROCESS DRY MATERIAL GRINDING GRANULATION Dry material (10% moisture) is taken to a second mill to reduce its particle size again. Shavings are pressed in pellet form with a final moisture around 5%.

7 ACTIVA INDUSTRIA 4.0 PILOT October 2016: 25 Spanish companies selected to participate in the Active Industry 4.0 Pilot November 2016: All the information regarding the situation of the company in terms of business strategy, processes, organization and people, infrastructures, products and services is analyzed. With all this, a Situation Diagnosis is prepared. February 2017: Starting from the basis of the previous diagnosis, several opportunities are identified and a it s defined the Transformation Plan which will take into account the opportunities prioritized in the previous report. March 2017: The execution of the Transformation Plan starts. June 2018: Ending of the first phase and starting of the second execution phase Oct Nov Dic Ene Feb Mar Abr May Jun Jul I4.0 Pilot Beginning Diagnosis Transformation Plan Start of the execution Plan Workshops

8 DEFINE AND START THE 4.0 INDUSTRY TRANSFORMATION PLAN The first stage of the transformation plan consisted in identifying the opportunities that could solve the company's improvement points. Once identified, an execution proposal and a timeline of the prioritized actions were made. OPORTUNIDAD dic-16 ene-17 feb-17 mar-17 abr-17 may-17 jun-17 jul-17 ago-17 sep-17 oct-17 nov-17 dic-17 ene-18 feb-18 mar-18 abr-18 may-18 jun-18 Definición Plan Tecnologías en campa (stocks y proceso) Cubierta acopios material corteza Secadero inteligente (Sensorización) Robotizar carga producto final Identificador códigos saco individual Desarrollo App Gestión del cambio. Formación I4.0 Interoperabilidad entre sistemas Optim. sensorización máquinas/procesos Innovación: Colaboraciones

9 PLANT SENSOR SYSTEM Challenge: Obtaining data in real time of the critical parameters of the factory. Description: Several types of sensors are installed that transmit information to our production software (SCADA). Temperature sensors (PT100, thermocouple and thermographic camera): located in strategic points of the process, allow us to perform a temperature control of the equipment and gases of the dryer. Humidity sensors: the moisture of the final product is a quality parameter that requires constant control. Pressure sensors: allow us to know the flow of air that circulates through the ducts of the dryer. Spin sensor: detect if there is movement in certain parts of the equipment. Position detector (limit switches): they allow us to know the position of the mechanical elements. Level detector (rotary, pendular and ultrasound): report the amount of located material. Solenoid valves: control the flow of oil to activate the hydraulic cylinders. Frequency inverters: they allow to vary the speed at which the motor works depending on the load, which allows an improvement in energy efficiency. Scales: assess the efficiency of the process by knowing exactly the quantity of raw material used. This information is processed in the SCADA, where the data analysis, control charts and process adjustments are made.

10 AUTOMATION OF PRODUCTION The information provided by the SENSORS and CONTROL GRAPHS is analyzed, adjusting the process based on the conclusions obtained. - Automatic intervention: adjustment of work parameters through the SCADA, without intervention of the operator. - Manual intervention: the operator visualize the information in real time and can manually modify some parameters.

11 OPTIMIZE DRYING LINE MAKE IT SMART: Challenge: Optimize the dryer process. Description: The two dryers have been equipped with the necessary sensors to automate their operation. BOILER DRYER Fireplace Temp, Exit Temp, Fuel feed, Fan speed, Gas flow Work Temp, Pressure, Raw material Humidity, Fueling Speed Despite the existing variability in the drying process (weather conditions, characteristics of the raw material, fuel characteristics, performance of the previous processes...) we have achieved: Maximize production Minimize consume (electrical, raw materials ecc) Guarantee the final product quality Control of the process in real time: immediate detection of deviations (alarms) and fast intervention to return to normal operating parameters

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13 NEW CHALLENGE : PREDICTIVE MAINTENANCE Overview: The factory works 24h, 365 days per year, which implies that an unexpected stop has great implications. Nowadays we make two types of maintenance: - Corrective maintenance: when a failure is detected or there is an unexpected stop of the equipment - Preventive maintenance: visual checks, tighten the screws, lubrication; according to fabricant directions or maintenance team experience. Challenge: Reduce unscheduled stops Description: The objective is to analyze and exploit the available data which are production variables and historical equipment failures in such a way that we detect patterns that help us to predict anomalous behavior of the equipment, anticipating possible failures.

14 NEW CHALLENGE : PREDICTIVE MAINTENANCE 1. IDENTIFY THE CRITICAL PATH Debarking Splinting Raw material grinding Boiling Storage Granulation Dry material grinding Drying

15 NEW CHALLENGE : PREDICTIVE MAINTENANCE 1. IDENTIFY THE CRITICAL PATH Debarking Splinting Raw material grinding Boiling Storage Granulation Dry material grinding DRYING The DRYING LINE will be considered as the SCOPE of this project, since the impact of a possible failure in production is highly critical and, in addition, the complexity of the process makes it difficult to detect possible failures or wear on the equipment.

16 NEW CHALLENGE : PREDICTIVE MAINTENANCE 2. COLLECT DATA DRYER Scada: Historical data file of representative variables Manual: Most significant production data filled by the operator) ERP: Preventive maintenance + workshop orders + costs applied to equipment

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19 NEW CHALLENGE : PREDICTIVE MAINTENANCE 2. COLLECT DATA DRYER Scada: Historical data file of representative variables Manual: Most significant production data filled by the operator ERP: Preventive maintenance + workshop orders + costs applied to equipment

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21 NEW CHALLENGE : PREDICTIVE MAINTENANCE 2. COLLECT DATA DRYER Scada: Historical data file of representative variables Manual: Most significant production data filled by the operator) ERP: Preventive maintenance + workshop orders + costs applied to equipment

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23 NEW CHALLENGE : PREDICTIVE MAINTENANCE 3. IDENTIFICATION OF SOURCES OF VARIABILITY Raw material initial humidity Fuel humidity (bark) Climatology: temperature and humidity Hot air flow controlled by the pressure applied inside the dryer Raw material flow controlled by feeding speed and stopping times of the mobile base Equipment conditions as the wear produces a loss of performance in the equipment

24 NEW CHALLENGE : PREDICTIVE MAINTENANCE 4. IDENTIFICATION OF PATTERNS AND MATHEMATICAL MODEL Experience: Indicators that conducts are damage: reduction of the production, the system demands higher temperatures and depressions Dryer s Performance drops when the material is accumulated on conducts wall. SCOPE: Model the performance of the dryer in order to determinate the normal conditions. By doing so, when production differs from the values considered normal, a stop can be scheduled to carry out the pertinent repairs.

25 THANK YOU FOR YOUR ATTENTION! Laura Vázquez Pardo