Periodic Reporting for period 3 - FACTLOG (Energy-aware Factory Analytics for Process Industries)
Période du rapport: 2022-05-01 au 2023-04-30
Digital transformation of the process industry is very challenging. The penetration and wide adoption of AI, Big Data and Internet of Things create some areas of automation and improved decision making. Aspects like energy consumption and process performance can be easily monitored and optimized with the use of Industry 4.0 concepts. For instance, industry sector accounted for 25.35% of the EU-28 total final energy consumption in 2015 being the 2nd most energy consuming sector behind transport. Energy consumption is a critical cost factor in the process industry and can be reduced in different ways: a) by transforming energy sources to “greener” and less fuel/fossil dependant, and b) by optimizing operations in a way that they can use less energy.
In the digital world, different tools can be used for optimized operations. The usage of Digital Twins is considered one of the most promising trends where we can model process industry assets in the digital world and be able to better monitor the performance and assess impact and test optimization scenarios. In addition, the use of AI can enhance the ability to understand behavioural trends, predict issues and possible anomalies. Predictive analytics offer a wide range of functionalities such as predictive maintenance, energy optimization and others, which result in significant cost reductions and energy efficiency.
FACTLOG addressed the above challenges by developing a technology pipeline based on the concept of Enhanced Digital Twins (ECT): Digital Twins with cognition capabilities, able to understand and alter their behaviour and enhanced with optimization capabilities. The FACTLOG offerings have been tested in four process industry environments.
In the digital world, different tools can be used for optimized operations. The usage of Digital Twins is considered one of the most promising trends where we can model process industry assets in the digital world and be able to better monitor the performance and assess impact and test optimization scenarios. In addition, the use of AI can enhance the ability to understand behavioural trends, predict issues and possible anomalies. Predictive analytics offer a wide range of functionalities such as predictive maintenance, energy optimization and others, which result in significant cost reductions and energy efficiency.
FACTLOG addressed the above challenges by developing a technology pipeline based on the concept of Enhanced Digital Twins (ECT): Digital Twins with cognition capabilities, able to understand and alter their behaviour and enhanced with optimization capabilities. The FACTLOG offerings have been tested in four process industry environments.
In the final period (May 2022-Apr 2023), we focused on refinement and further fine-tuning of the different modules towards producing the integrated FACTLOG system. Necessary customisations were necessary to address the comments received from the pilots, while particular emphasis was placed on investigating the main challenges pertaining to the integration of FACTLOG into new production environments, and thereupon facilitating new installations through specific technological choices and best practices. Moreover, the second round of pilot operations were successfully completed, showcasing the added-value of FACTLOG and, as such, validating the overall approach. This was mainly streamlined through dedicated workshops organised in each pilot, receiving overall very positive feedback. In addition, interviews were administered in each pilot to provide meaningful insights regarding the potential use of FACTLOG in industrial contexts. Finally, the overall solution was evaluated against measurable KPIs, substantiating FACTLOG offering in terms of the proposed business plan, but also individual exploitation plans.
The main results achieved from each pilot are presented below:
TUPRAS: aimed to improve detection and response for C2 and C5 deviations in the LPG production. With FACTLOG, TUPRAS estimated a projected value of increase product quality by > 6%, decrease of spec production by > 30%, and decrease product Failure Response Time by 50%.
BRC: aimed to improve energy efficiency in production scheduling. With FACTLOG, BRC estimated a projected value of reduction of total machinery daily downtime by 10% (approx. 2.5 hrs saving per day), a minimum time saving of 1 hour per day (40% reduction), 75% reduction in manual override.
PIA: aimed to improve energy efficiency in production scheduling. With FACTLOG, PIA estimated a projected value of 30% increase average warp length in meters, 25% increase of total production of fabrics in meters, 35% increase of weaving dept exploitation vs total production, 20% reduction of delivery time, 20% reduction of order fulfilment.
CONT: aimed to improve operational efficiency and optimize production with minimum idle times. With FACTLOG, it estimated a projected value of decrease machine downtime by > 35%, decrease ratio total maintenance/total operational costs by > 30%, and increase overall equipment efficiency by > 9%.
The main results achieved from each pilot are presented below:
TUPRAS: aimed to improve detection and response for C2 and C5 deviations in the LPG production. With FACTLOG, TUPRAS estimated a projected value of increase product quality by > 6%, decrease of spec production by > 30%, and decrease product Failure Response Time by 50%.
BRC: aimed to improve energy efficiency in production scheduling. With FACTLOG, BRC estimated a projected value of reduction of total machinery daily downtime by 10% (approx. 2.5 hrs saving per day), a minimum time saving of 1 hour per day (40% reduction), 75% reduction in manual override.
PIA: aimed to improve energy efficiency in production scheduling. With FACTLOG, PIA estimated a projected value of 30% increase average warp length in meters, 25% increase of total production of fabrics in meters, 35% increase of weaving dept exploitation vs total production, 20% reduction of delivery time, 20% reduction of order fulfilment.
CONT: aimed to improve operational efficiency and optimize production with minimum idle times. With FACTLOG, it estimated a projected value of decrease machine downtime by > 35%, decrease ratio total maintenance/total operational costs by > 30%, and increase overall equipment efficiency by > 9%.
Whereas in traditional companies Digital Twins are mostly used as “digital replicas” of physical assets that support better design of products (through simulations), data-driven manufacturing requires more “cognitive augmentation” of assets for enabling continuous process improvement. FACTLOG has a (cognition-like) ability for understanding/resolving the “unknown unknowns” and to “reason” about the problem (e.g. level of the tool degradation for each asset and the type of product/material) based on real-time data from the processes. This cognition capability is supported by the so-called “just-in-time simulation” of the process status in some suspicious/unusual situations in order to calculate the confidence that an unusual situation leads to an anomaly that should be resolved. It then incorporates optimisation methods enabling the Digital Twin to recover the actual system’s performance and resource efficiency.
Short-term decision support level implies real-time (and/or reactive) optimization, as the performance of a manufacturing process or control system depends mostly on its ability to rapidly adapt schedules to current circumstances. These dynamic (re-)optimization problems, where the optimization is performed gradually in specific time intervals and evolves dynamically in an effort to incorporate the occurrence of new information or the update of old ones, can be approached by reactive and proactive frameworks. Such approaches have been deployed in the FACTLOG pilots, facing a diversity of operational environment, from on-spec timely recovery of Liquefied Petroleum Gas (LPG) refinement to effective scheduling of textile weaving and to maintenance driven manufacturing.
FACTLOG’s progress beyond existing knowledge relies not only on optimisation and analytics as individual components but on their tight integration that relies on process modelling and knowledge graphs to ensure replicability and transferability to manufacturing environments analogous or closely related to the ones examined within the FACTLOG pilots.
In socio-economic terms, FACTLOG introduces enhanced automation that will support process industries and manufacturing in terms of cost-reduction and resource efficiency, thus bringing tangible benefits to companies associated with FACTLOG through its industrial clusters and associations.
Short-term decision support level implies real-time (and/or reactive) optimization, as the performance of a manufacturing process or control system depends mostly on its ability to rapidly adapt schedules to current circumstances. These dynamic (re-)optimization problems, where the optimization is performed gradually in specific time intervals and evolves dynamically in an effort to incorporate the occurrence of new information or the update of old ones, can be approached by reactive and proactive frameworks. Such approaches have been deployed in the FACTLOG pilots, facing a diversity of operational environment, from on-spec timely recovery of Liquefied Petroleum Gas (LPG) refinement to effective scheduling of textile weaving and to maintenance driven manufacturing.
FACTLOG’s progress beyond existing knowledge relies not only on optimisation and analytics as individual components but on their tight integration that relies on process modelling and knowledge graphs to ensure replicability and transferability to manufacturing environments analogous or closely related to the ones examined within the FACTLOG pilots.
In socio-economic terms, FACTLOG introduces enhanced automation that will support process industries and manufacturing in terms of cost-reduction and resource efficiency, thus bringing tangible benefits to companies associated with FACTLOG through its industrial clusters and associations.