Final Report Summary - ENERGY-SMARTOPS (Energy savings from smart operation of electrical, process and mechanical equipment)
1 INTRODUCTION
1.1 PROJECT CONTEXT AND AIMS:
The drive across the world towards energy efficiency and reduction of carbon dioxide emissions is leading to new industrial processes and new ways of operating existing processes. In particular, the control and operation of industrial processes is becoming more integrated giving new opportunities for energy saving through equipment management, automation, and optimization.
Scientists in the EU-funded ENERGY-SMARTOPS project addressed the integrated control and operation of processes, rotating machinery and electrical equipment. The aim was to understand and demonstrate how integrated operation can achieve energy savings by optimization of the system as a whole. The project generated and tested creative ideas for energy savings in large scale industrial sites that were implemented in practical case studies.
1.2 PROJECT DESCRIPTION
ENERGY-SMARTOPS combined in-depth understanding of practical system operation with analysis from first-principles. The interdisciplinary team included experts in electrical machinery and power electronics, compressors and pumps, modelling and optimization, instrumentation, signal analysis, equipment condition monitoring, and automation of oil and gas, steel and chemical processes. The project team worked on (i) equipment and process monitoring, integrating multiple measurements from the process, mechanical and electrical sub-systems, (ii) integrated automation capturing information from all three subsystems, and devising new algorithms that explicitly manage the interfaces and interactions between them, and (iii) optimization to provide energy savings by integration of operations across the process-mechanical-electrical interfaces.
The key impact of the technology and solutions developed by ENERGY-SMARTOPS researchers will be energy savings achieved through the integrated operation of the process, mechanical and electrical domains. The likelihood of results and novel technologies being exploited for commercial use is high given the involvement of industrial partners. Achieving energy savings of 20% or more is a target for the 2020 EU energy strategy, and ENERGY-SMARTOPS is helping industrial sites attain this target.
The ENERGY-SMARTOPS project involved end-user companies (BASF, Acciai Speciali Terni, Statoil), companies that supply technology and training (ABB R&D in Norway, Poland and Germany, ESD Simulation Training Ltd), and universities (Imperial College London, Cranfield University, ETH Zurich, Politechnika Krakowska, Carnegie Mellon University).
ENERGY SMARTOPS has trained a cohort of Early Stage Researchers (ESRs) in the above areas in a variety of ways including doctoral studies towards a PhD, industrial training, and short courses. The scientific work has been carried out by the ESRs under the supervision of a team of highly experienced research leaders from academia and industry.
Several ideas originating in the project are being developed further and implemented. Three patent applications are in progress.
Web sites giving more information about activities, events of the ENERGY-SMARTOPS Consortium and the outcomes of its research can be accessed at www3.imperial.ac.uk/smartops/description and www.energy-smartops.eu.
1.3 ENERGY-SMARTOPS AS AN FP7 MARIE CURIE PEOPLE PROGRAMME
The ENERGY SMARTOPS Consortium has undertaken a lively and productive programme of training and networking of Early Stage Researchers, as may be seen in the “Energy-Smartops people” and “Energy-Smartops secondments” posters available at www3.imperial.ac.uk/smartops/description. All researchers have undertaken PhD studies during their Marie Curie Fellowships as well as an extensive programme of technical and transferrable skills training. They have gone on to good careers carrying their training and expertise with them.
ENERGY-SMARTOPS is more than just the outputs of individual person-months of work because the researchers have built upon the relationships developed within the Initial Training Network. There have been several joint publications, many of them taking an interdisciplinary approach which would not otherwise have been possible. The project’s researchers had a strong showing in the 24th European Symposium on Computer Aided Process Engineering (ESCAPE 24) in Budapest, where they organized and presented a well-attended Special Session on “Results of the ENERGY-SMARTOPS project”. A further special Session was presented at the 2014 UKACC 10th International Conference on Control. The publicity flyers for these can be accessed at www3.imperial.ac.uk/smartops/publications.
2 OUTCOMES AND NEW KNOWLEDGE FROM THE PROJECT
The ENERGY-SMARTOP project was organized into five Work Packages (WPs). The coordinators of the WPs have prepared WP reports which have been uploaded with this final report and may also be viewed at www3.imperial.ac.uk/smartops/description. The last page of each WP report lists the Intended Outcomes, Methodology Developed and Measures of Success. These comments are presented below
2.1 WP1: Electromachinery:
2.1.1 Objectives
* Modelling of electromechanical system with effects of interactions for diagnostic purpose
* Undertake data capture, conditioning and analysis with advance signal processing methods for diagnosis of electrical/mechanical interactions
* Create diagnostic algorithms based on intelligent calculation (neural network, fuzzy logic, pattern recognition) for machines assessment in industry electric drives.
* Develop a systematic framework for increasing diagnostic reliability through combination of global diagnostic signals or diagnostic indicators
2.1.2 Intended outcome
Increasingly, electrical motors are at the heart of a wide variety of industrial processes.
Induction machines are the workhorse of industrial processes. It is necessary to understand and exploit their dynamics in order to successfully diagnose their health.
Synchronous machines are typically used to drive critical processes. It was intended to identify new techniques for monitoring such machines.
Electrical machines form part of a larger system of interacting components. This workpackage also aimed to utilize these interactions to achieve more comprehensive condition monitoring.
Increasing confidence levels in condition monitoring techniques makes condition-based maintenance more feasible. Such maintenance strategies can result in reduced energy losses through maintenance actions.
2.1.3 Methodology developed
Preparation of models of induction machines in both healthy and faulty states. Models have been validated using specially-prepared experimental test stands.
Design, implementation and testing of advanced measurement system for synchronous machines, including non-standard sensing equipment. Collection of a large database of signals recorded from the machine under various health states. This allows new advanced diagnostics algorithms to be developed.
Development of a multi-sensor data fusion approach for combining diagnostics signals and events from multiple sources. Approaches based on Artificial Neural Networks and Bayesian Networks have been created and validated using real data.
2.1.4 Measure of success
New diagnostic algorithms allow electrical faults to be distinguished from mechanical faults. This reduces the likelihood of misdiagnosis, leading to more confidence in monitoring results. Energy that would have otherwise been used to investigate misdiagnoses is thus saved.
The health of synchronous machines is relatively difficult to diagnose using standard methods. New measurement approaches and algorithms have been developed which allow synchronous machine faults to be identified. This reduces the need for inefficient periodic maintenance stops to the process.
Bayesian Network approach for combining condition monitoring results from multiple subsystems, allows the most-likely source of a fault to be identified, resulting in reduced downtime and more accurate diagnosis. Hence less energy is expended solving maintenance issues.
2.2 WP2: Turbomachinery
2.2.1 Objectives
Energy savings in compressor operation by means of:
* Develop scalable and complete equipment monitoring systems
* Devise new algorithms for overall performance monitoring and control
* Study ways that energy savings can be achieved
* Tasks to achieve the aims are:
* Adaptive monitoring of condition and performance of centrifugal compressors
* Optimization of centrifugal compressor operation and maintenance
* Aerodynamic impact of fouling in centrifugal compressors
* Control systems for centrifugal CO2 compressors
2.2.2 Intended outcome
Methods and algorithms aim to reduce the energy usage for gas compression. Optimization frameworks increase the system efficiency and reduce overall operational costs using monitoring information.
The monitoring information comes from algorithms which implement physical knowledge and reveal the gradual degradation of the compressors.
The outcomes include guidelines regarding the operation of supercritical carbon dioxide compressors and suggestions on the best recycle configuration of compressors.
2.2.3 Methodology developed
A real time optimization scheme deals with the model uncertainty as the models are updated online. A Non-Linear Programming (NLP) optimization problem is solved online in real time and it minimizes the operation cost.
A mixed integer linear programming optimization model describes the condition based maintenance and optimal operation for long term scheduling. This approach includes the adaptive monitoring of the health-state and performance of compressors.
Performance estimation based on experimental calculations of fouling in centrifugal compressors has been studied as well.
The response of the control schemes of compressors analysed by means of graphical representation and dimensionless indicators.
2.2.4 Measure of success
The real time optimization (RTO) can decrease the power consumption of the BASF air compressor station compared to equal split industrial policy.
The physical models to be used for online performance monitoring methods can achieve accuracy of +/- 1%.
The use of the scheduling approach can achieve 10% reduction in the total costs of the operation when the maintenance of the compressors is considered as a degree of freedom into the optimization problem.
The analysis on the recycle of supercritical CO2 compressor showed reduction of energy consumption by 37% and 7.3% for inlet and outer disturbances respectively.
2.3 WP3: Maintenance and Diagnosis
2.3.1 Objectives
Prototypic reactive maintenance planning algorithms for industrial use cases will be developed. The algorithms will be based on integrated models of the electrical and the processing subsystems of the plants.
To develop and optimize new methodologies for predictive equipment condition monitoring through statistical process monitoring of multivariate data typically employed for monitoring process, electrical and mechanical machinery.
Technology, methods and instrumentation for automatic detection of leakage in safety valves.
To define a new concept for representation of plant and equipment topology that will enable more efficient collaboration between personnel from the electrical, process and mechanical disciplines.
2.3.2 Intended outcome
Develop and validate improved algorithms for predictive process monitoring that can provide information about the presence of faults, their origin, and their impact on the system performance.
Define new concepts for representing plant and equipment dependencies over process, electrical and mechanical domains that can support collaboration during fault diagnosis and risk analysis.
Develop an optimization based approach for the joint maintenance and production scheduling of process plants, by explicitly taking into account information on system degradation coming from a predictive process monitoring system.
2.3.3 Methodology developed
Data-based methods such as Canonical Variate Analysis (CVA) were used for fault detection, diagnosis, and estimation of performance degradation. These methods were validated in an experimental multiphase flow facility operated under changing operational conditions.
Parsing algorithms to extract process connectivity from disparate data sources were developed. A graph data model proposed for integrating and storing connectivity; and visual analytics were applied in the design of a graphical user interface to be used in connectivity analysis.
A mixed integer programming approach (MILP) has been used to model the joint maintenance and production scheduling problem. A multi-time scale and aggregated approach have been used to address the different time dynamics between maintenance and production as well as to keep the problem solvable from a computational point of view.
2.3.4 Measure of success
CVA successfully detected and diagnosed different process faults seeded in the experimental plant. The methodology proposed quantified an energy waste of 8.15kWh over 80 min. caused by a partial pipe blockage.
Connectivity graphs covering different domains allow much faster understanding of how faults could propagate between systems and sub-systems and can point to the root-cause faster than using manual analysis. Energy and cost are saved due to reduced down-time.
Production and maintenance tasks are seamlessly planned over the scheduling horizon in order to meet production requests. The operation mode of plant equipment is varied accordingly and to meet the long term maintenance objectives.
2.4 WP4: Energy optimization
2.4.1 Objectives
Developing robust advanced control techniques for electrical drives, taking account of optimal efficiency by:
* Identification of accurate models that consider previously unaccounted effects such as iron losses and machine saturation
* Finding the best control policy to improve efficiency for a single machine given the requirements from the process (e.g. torque and speed)
* Robust control techniques to account for variations in model parameters for constrained control problems
Implementing a practical case study of the control algorithms by:
* Developing a two-motor test rig to emulate process machinery (e.g. a compressor)
* Demonstrations of real-time control, especially for compressors and variable speed drives.
2.4.2 Intended outcome
Developing advanced control techniques for electrical drives, taking account of optimal efficiency. This requires improved modelling of previously unaccounted effects
Given a single machine and the mechanical requirements from the process, we look for the best control policy to improve efficiency
Consider a case study as practical implementation of the control algorithms, in particular a two-motor test rig in which a process can be emulated (e.g. a compressor) . The case study is particularly suited for compressors and variable speed drives
2.4.3 Methodology developed
Accurate estimation of copper and iron losses are necessary but unavailable from the constructor. Hence we derive a procedure to build the model of losses
The estimate of iron losses is considerable in magnitude, thus we consider them in control strategies
Robust control algorithms take into account uncertaintes and performance bounds
Testing controllers for electric drives connected to compressors can be done on scaled test benches
The compressor is emulated by a software which replicates the dynamics of a real compressor
2.4.4 Measure of success
Accounting for the iron losses allow us to achieve up to 20% power losses reduction on the tested 1HP induction motor. This is achieved by advanced control techniques
The efficiency increases accordingly up to 3%. This reduces sensibly the stress on the motor and increases the machine rating
Control techniques for compressor reduce the risk of instabilities during the operations. This is achieved through combined backstepping control and Model Predictive Control techniques. Recycle valves, usually present in modern plants, can be successfully considered.
2.5 WP5: Electricity optimization
2.5.1 Objectives
WP5 aims to deliver technology prototypes for energy/cost savings at the scale of production processes:
* By analysing the interaction between large and variable industrial loads and the electrical grid
* By developing and implementing scheduling optimization methods to integrate the production of a steel plant.
* By developing a framework and methods for demand response of energy-intensive processes
Implementing a practical case study of the control algorithms by:
2.5.2 Intended outcome
Create the means to deal with increasing volatility in production, energy and raw material availability. The key aspect is to ensure seamless integration and availability of data and information across the plant, enabling user interaction and connection to optimization solutions.
Bridge the gap between production, energy management and maintenance. This can be enabled through intelligent and adaptable advanced analysis tools.
Enable energy and cost savings through optimization. This will require new approaches where traditional planning models must be expanded to cover larger problem instances and energy consumption (industrial demand-side management).
2.5.3 Methodology developed
Level-2 systems have been integrated and all relevant information can be collected and visualized. Flexible user interfaces linked to optimization enable the possibility to perform planning tasks manually, partly or fully supported by the optimization tools.
Data mining applying statistical pattern recognition and signal processing has made it possible to identify maintenance needs more efficiently.
Mixed integer linear programming optimization models have been studied to include production scheduling and energy volatility, i.e. availability and price. Continuous and discrete-time scheduling models using various decomposition schemes have been developed, for instance applying mean value cross decomposition.
2.5.4 Measure of success
The use of scheduling optimization improved the coordination between different production stages in the melt shop of a stainless steel plant, lowered the hold-up times and increased the production rate.
The potential benefits have been estimated to be:
* 5% reduction in energy consumption,
* 4-5% in lead times,
* 2-3% in inventory levels.
The potential impact of intelligent energy-aware scheduling of processes has been evaluated to enable a reduction of electricity cost of 2-20%.
The use of an optimized coordination policy between melt shop and rolling mill reduce thermal losses. The anticipated increase of hot charging ratio is up to +22%
3 IMPACT
3.1 OVERVIEW
ENERGY-SMARTOPS is an FP7 Marie Curie Initial Training Network (ITN) project. The Early-Stage Researchers recruited are known as “Marie Curie Fellows”. The quote below is description of the ITN scheme from the FP7 People Work programme:
“This action aims to improve career perspectives of early-stage researchers in both public and private sectors, thereby making research careers more attractive to young people. This will be achieved through a trans-national networking mechanism, aimed at structuring the existing high-quality initial research training capacity throughout Member States and associated countries. Direct or indirect involvement of organisations from different sectors, including (lead-) participation by private enterprises in appropriate fields, is considered essential in the action. In particular, the action aims to add to the employability of the recruited researchers through exposure to both academia and enterprise, thus extending the traditional academic research training setting and eliminating cultural and other barriers to mobility.”
This section aims to show how the ENERGY-SMARTOPS project has delivered the impacts expected from an ITN project.
3.2 SOCIOECONOMIC IMPACT
3.2.1 Impact on careers and skills:
The support of the ITN scheme for the project, especially with its high level of industrial engagement, had a demonstrated impact on the careers of its Early Stage Researchers. Direct evidence is that ten of the 16 ESRs have continued further employment with their host organizations. Four others have moved into excellent jobs involving a different sector (one has moved from university to industry, one moved from industry to become a university lecturer, one is a self-employed entrepreneur running his own business, and one has moved to a different industry sector). The two remaining ESRs are currently job seeking while finishing their PhDs.
The researchers have had many benefits from their Marie Curie Fellowships including:
*Access to quality of training and research supervision and career development
*Access to high quality research facilities, laboratories and industrial equipment
*A solid preparation on the primary subjects of research to doctoral level
*Research outputs especially publications and patents
*Development of complementary skills including team working, leadership, and project management
*International mobility experience, both when taking up their appointments and during secondments
*Intersectoral experience by working with ERS from the other sector during secondments
*Forging a strong professional network with other ESRs and the managers/supervisors, which will continue after the project
3.2.2 Strengthening European innovation capacity:
The ENERGY-SMARATOPS partners understood the need to avoid fragmentation and duplication of work in different organizations and selected the ITN scheme as a programme to bring together researchers with similar interests and complementary activities. Its activities strengthen European innovation capacity by means of:
* Increasing the competitiveness of European companies and contributing to the Europe 2020 strategy for smart, sustainable and inclusive growth
->using state-of-the-art knowledge from academia through planned secondments and the knowledge base built during the lifetime of the ITN to support the development of new technologies, tools and methodologies;
->developing mutual trust between universities and the industry, leading to openness in sharing the research challenges and case studies;
->taking research ideas from the laboratory for rapid prototyping and testing in industrial partners up to TRL 5, in readiness for larger scale demonstration at higher TRL levels.
*Training a cohort of researchers who can move comfortably between academia and industry
->education of future scientific leaders in Europe with a strong academic background;
->giving the ESRs experience of generating creative and potentially innovative ideas;
->enhancing the teaching activities of universities through involvement of industry.
*Making strategic extensions to existing academic and industrial collaborations, thus strengthening a Europe-wide research and development capability.
The project enabled local activity to operate at a higher level as a European-wide initiative enabling individual research outputs to have a synoptic and coordinated impact. A public web-based dissemination site has captured and collected together the findings and experiences of the project, descriptions of the ESRs and a list of published papers.
3.2.3 Lasting R&D collaboration:
The ENERGY-SMARTOPS project has established new long-term collaborations and friendships between the Consortium Partners which enabled them to do substantial pieces of work together. Many of the partners are continuing their collaborations after the end of the project. A specific future plan is to propose a new ITN to explore the exciting new ideas concerning condition-based maintenance that have emerged from ENERGY-SMARTOPS.
3.2.4 Impact on European industrial energy efficiency:
ENERGY-SMARTOPS addressed the European process industries, one of the largest and most dynamic industrial sectors in the EU. In particular, it targeted the efficient and sustainable operation of the installed assets of the process industries. The research tasks align with well with topics identified in several EU roadmaps [1]-[3].
[1] European Commission, 2011, A resource-efficient Europe – Flagship initiative under the Europe 2020 Strategy, Communication from the Commission to the European Parliament, http://ec.europa.eu/resource-efficient-europe/pdf/resource_efficient_europe_en.pdf
[2] SPIRE, 2013, Sustainable Process Industry, Multi-Annual Roadmap, pub: European Commission Directorate G Industrial Technologies, Brussels.
[3] ProcessIT, 2013, European Roadmap for Industrial Process Automation, Ed: P. Lingman, pub: Artemis Industry Association, Eindhoven, The Netherlands.
The ENERGY-SMARTOPS project has made contributions to the energy efficiency of European industry, as outlined in the Outcomes and New Knowledge section above. As well as a major impact on the state-of-the art by means of publications, the intersectorial secondments have facilitated technology transfer because the partners include companies that produce products (the end-users of technology), the universities, and the R&D organizations of the technology companies who have the capacity to develop and commercialize research outputs and turn them into products. At the end of the project, there are more than 70 published papers and three patent applications arising from the work of the ESRs. There are, additionally, several significant new insights into how energy savings can be gained in the process industries.
3.2.5 Gender aspects:
It is generally not possible in a project involving R&D industrial and automation engineers to achieve an equal balance between men and women, because the pool of male candidates who meet the essential qualifications is larger than the pool of qualified female candidates. Nevertheless, three of the ten partner organizations had female Scientists-in-charge, including the coordinator at Imperial who is female. Two of the 16 ESRs were female.
All partners are committed to providing a positive and inclusive working environment in order to bring out the full potential of all staff. The consortium partners were aware of the targets and provisions of the Commission Staff working document Sec_2005_379 Women and Science: Excellence and Innovation – Gender Equality in Science.
1.1 PROJECT CONTEXT AND AIMS:
The drive across the world towards energy efficiency and reduction of carbon dioxide emissions is leading to new industrial processes and new ways of operating existing processes. In particular, the control and operation of industrial processes is becoming more integrated giving new opportunities for energy saving through equipment management, automation, and optimization.
Scientists in the EU-funded ENERGY-SMARTOPS project addressed the integrated control and operation of processes, rotating machinery and electrical equipment. The aim was to understand and demonstrate how integrated operation can achieve energy savings by optimization of the system as a whole. The project generated and tested creative ideas for energy savings in large scale industrial sites that were implemented in practical case studies.
1.2 PROJECT DESCRIPTION
ENERGY-SMARTOPS combined in-depth understanding of practical system operation with analysis from first-principles. The interdisciplinary team included experts in electrical machinery and power electronics, compressors and pumps, modelling and optimization, instrumentation, signal analysis, equipment condition monitoring, and automation of oil and gas, steel and chemical processes. The project team worked on (i) equipment and process monitoring, integrating multiple measurements from the process, mechanical and electrical sub-systems, (ii) integrated automation capturing information from all three subsystems, and devising new algorithms that explicitly manage the interfaces and interactions between them, and (iii) optimization to provide energy savings by integration of operations across the process-mechanical-electrical interfaces.
The key impact of the technology and solutions developed by ENERGY-SMARTOPS researchers will be energy savings achieved through the integrated operation of the process, mechanical and electrical domains. The likelihood of results and novel technologies being exploited for commercial use is high given the involvement of industrial partners. Achieving energy savings of 20% or more is a target for the 2020 EU energy strategy, and ENERGY-SMARTOPS is helping industrial sites attain this target.
The ENERGY-SMARTOPS project involved end-user companies (BASF, Acciai Speciali Terni, Statoil), companies that supply technology and training (ABB R&D in Norway, Poland and Germany, ESD Simulation Training Ltd), and universities (Imperial College London, Cranfield University, ETH Zurich, Politechnika Krakowska, Carnegie Mellon University).
ENERGY SMARTOPS has trained a cohort of Early Stage Researchers (ESRs) in the above areas in a variety of ways including doctoral studies towards a PhD, industrial training, and short courses. The scientific work has been carried out by the ESRs under the supervision of a team of highly experienced research leaders from academia and industry.
Several ideas originating in the project are being developed further and implemented. Three patent applications are in progress.
Web sites giving more information about activities, events of the ENERGY-SMARTOPS Consortium and the outcomes of its research can be accessed at www3.imperial.ac.uk/smartops/description and www.energy-smartops.eu.
1.3 ENERGY-SMARTOPS AS AN FP7 MARIE CURIE PEOPLE PROGRAMME
The ENERGY SMARTOPS Consortium has undertaken a lively and productive programme of training and networking of Early Stage Researchers, as may be seen in the “Energy-Smartops people” and “Energy-Smartops secondments” posters available at www3.imperial.ac.uk/smartops/description. All researchers have undertaken PhD studies during their Marie Curie Fellowships as well as an extensive programme of technical and transferrable skills training. They have gone on to good careers carrying their training and expertise with them.
ENERGY-SMARTOPS is more than just the outputs of individual person-months of work because the researchers have built upon the relationships developed within the Initial Training Network. There have been several joint publications, many of them taking an interdisciplinary approach which would not otherwise have been possible. The project’s researchers had a strong showing in the 24th European Symposium on Computer Aided Process Engineering (ESCAPE 24) in Budapest, where they organized and presented a well-attended Special Session on “Results of the ENERGY-SMARTOPS project”. A further special Session was presented at the 2014 UKACC 10th International Conference on Control. The publicity flyers for these can be accessed at www3.imperial.ac.uk/smartops/publications.
2 OUTCOMES AND NEW KNOWLEDGE FROM THE PROJECT
The ENERGY-SMARTOP project was organized into five Work Packages (WPs). The coordinators of the WPs have prepared WP reports which have been uploaded with this final report and may also be viewed at www3.imperial.ac.uk/smartops/description. The last page of each WP report lists the Intended Outcomes, Methodology Developed and Measures of Success. These comments are presented below
2.1 WP1: Electromachinery:
2.1.1 Objectives
* Modelling of electromechanical system with effects of interactions for diagnostic purpose
* Undertake data capture, conditioning and analysis with advance signal processing methods for diagnosis of electrical/mechanical interactions
* Create diagnostic algorithms based on intelligent calculation (neural network, fuzzy logic, pattern recognition) for machines assessment in industry electric drives.
* Develop a systematic framework for increasing diagnostic reliability through combination of global diagnostic signals or diagnostic indicators
2.1.2 Intended outcome
Increasingly, electrical motors are at the heart of a wide variety of industrial processes.
Induction machines are the workhorse of industrial processes. It is necessary to understand and exploit their dynamics in order to successfully diagnose their health.
Synchronous machines are typically used to drive critical processes. It was intended to identify new techniques for monitoring such machines.
Electrical machines form part of a larger system of interacting components. This workpackage also aimed to utilize these interactions to achieve more comprehensive condition monitoring.
Increasing confidence levels in condition monitoring techniques makes condition-based maintenance more feasible. Such maintenance strategies can result in reduced energy losses through maintenance actions.
2.1.3 Methodology developed
Preparation of models of induction machines in both healthy and faulty states. Models have been validated using specially-prepared experimental test stands.
Design, implementation and testing of advanced measurement system for synchronous machines, including non-standard sensing equipment. Collection of a large database of signals recorded from the machine under various health states. This allows new advanced diagnostics algorithms to be developed.
Development of a multi-sensor data fusion approach for combining diagnostics signals and events from multiple sources. Approaches based on Artificial Neural Networks and Bayesian Networks have been created and validated using real data.
2.1.4 Measure of success
New diagnostic algorithms allow electrical faults to be distinguished from mechanical faults. This reduces the likelihood of misdiagnosis, leading to more confidence in monitoring results. Energy that would have otherwise been used to investigate misdiagnoses is thus saved.
The health of synchronous machines is relatively difficult to diagnose using standard methods. New measurement approaches and algorithms have been developed which allow synchronous machine faults to be identified. This reduces the need for inefficient periodic maintenance stops to the process.
Bayesian Network approach for combining condition monitoring results from multiple subsystems, allows the most-likely source of a fault to be identified, resulting in reduced downtime and more accurate diagnosis. Hence less energy is expended solving maintenance issues.
2.2 WP2: Turbomachinery
2.2.1 Objectives
Energy savings in compressor operation by means of:
* Develop scalable and complete equipment monitoring systems
* Devise new algorithms for overall performance monitoring and control
* Study ways that energy savings can be achieved
* Tasks to achieve the aims are:
* Adaptive monitoring of condition and performance of centrifugal compressors
* Optimization of centrifugal compressor operation and maintenance
* Aerodynamic impact of fouling in centrifugal compressors
* Control systems for centrifugal CO2 compressors
2.2.2 Intended outcome
Methods and algorithms aim to reduce the energy usage for gas compression. Optimization frameworks increase the system efficiency and reduce overall operational costs using monitoring information.
The monitoring information comes from algorithms which implement physical knowledge and reveal the gradual degradation of the compressors.
The outcomes include guidelines regarding the operation of supercritical carbon dioxide compressors and suggestions on the best recycle configuration of compressors.
2.2.3 Methodology developed
A real time optimization scheme deals with the model uncertainty as the models are updated online. A Non-Linear Programming (NLP) optimization problem is solved online in real time and it minimizes the operation cost.
A mixed integer linear programming optimization model describes the condition based maintenance and optimal operation for long term scheduling. This approach includes the adaptive monitoring of the health-state and performance of compressors.
Performance estimation based on experimental calculations of fouling in centrifugal compressors has been studied as well.
The response of the control schemes of compressors analysed by means of graphical representation and dimensionless indicators.
2.2.4 Measure of success
The real time optimization (RTO) can decrease the power consumption of the BASF air compressor station compared to equal split industrial policy.
The physical models to be used for online performance monitoring methods can achieve accuracy of +/- 1%.
The use of the scheduling approach can achieve 10% reduction in the total costs of the operation when the maintenance of the compressors is considered as a degree of freedom into the optimization problem.
The analysis on the recycle of supercritical CO2 compressor showed reduction of energy consumption by 37% and 7.3% for inlet and outer disturbances respectively.
2.3 WP3: Maintenance and Diagnosis
2.3.1 Objectives
Prototypic reactive maintenance planning algorithms for industrial use cases will be developed. The algorithms will be based on integrated models of the electrical and the processing subsystems of the plants.
To develop and optimize new methodologies for predictive equipment condition monitoring through statistical process monitoring of multivariate data typically employed for monitoring process, electrical and mechanical machinery.
Technology, methods and instrumentation for automatic detection of leakage in safety valves.
To define a new concept for representation of plant and equipment topology that will enable more efficient collaboration between personnel from the electrical, process and mechanical disciplines.
2.3.2 Intended outcome
Develop and validate improved algorithms for predictive process monitoring that can provide information about the presence of faults, their origin, and their impact on the system performance.
Define new concepts for representing plant and equipment dependencies over process, electrical and mechanical domains that can support collaboration during fault diagnosis and risk analysis.
Develop an optimization based approach for the joint maintenance and production scheduling of process plants, by explicitly taking into account information on system degradation coming from a predictive process monitoring system.
2.3.3 Methodology developed
Data-based methods such as Canonical Variate Analysis (CVA) were used for fault detection, diagnosis, and estimation of performance degradation. These methods were validated in an experimental multiphase flow facility operated under changing operational conditions.
Parsing algorithms to extract process connectivity from disparate data sources were developed. A graph data model proposed for integrating and storing connectivity; and visual analytics were applied in the design of a graphical user interface to be used in connectivity analysis.
A mixed integer programming approach (MILP) has been used to model the joint maintenance and production scheduling problem. A multi-time scale and aggregated approach have been used to address the different time dynamics between maintenance and production as well as to keep the problem solvable from a computational point of view.
2.3.4 Measure of success
CVA successfully detected and diagnosed different process faults seeded in the experimental plant. The methodology proposed quantified an energy waste of 8.15kWh over 80 min. caused by a partial pipe blockage.
Connectivity graphs covering different domains allow much faster understanding of how faults could propagate between systems and sub-systems and can point to the root-cause faster than using manual analysis. Energy and cost are saved due to reduced down-time.
Production and maintenance tasks are seamlessly planned over the scheduling horizon in order to meet production requests. The operation mode of plant equipment is varied accordingly and to meet the long term maintenance objectives.
2.4 WP4: Energy optimization
2.4.1 Objectives
Developing robust advanced control techniques for electrical drives, taking account of optimal efficiency by:
* Identification of accurate models that consider previously unaccounted effects such as iron losses and machine saturation
* Finding the best control policy to improve efficiency for a single machine given the requirements from the process (e.g. torque and speed)
* Robust control techniques to account for variations in model parameters for constrained control problems
Implementing a practical case study of the control algorithms by:
* Developing a two-motor test rig to emulate process machinery (e.g. a compressor)
* Demonstrations of real-time control, especially for compressors and variable speed drives.
2.4.2 Intended outcome
Developing advanced control techniques for electrical drives, taking account of optimal efficiency. This requires improved modelling of previously unaccounted effects
Given a single machine and the mechanical requirements from the process, we look for the best control policy to improve efficiency
Consider a case study as practical implementation of the control algorithms, in particular a two-motor test rig in which a process can be emulated (e.g. a compressor) . The case study is particularly suited for compressors and variable speed drives
2.4.3 Methodology developed
Accurate estimation of copper and iron losses are necessary but unavailable from the constructor. Hence we derive a procedure to build the model of losses
The estimate of iron losses is considerable in magnitude, thus we consider them in control strategies
Robust control algorithms take into account uncertaintes and performance bounds
Testing controllers for electric drives connected to compressors can be done on scaled test benches
The compressor is emulated by a software which replicates the dynamics of a real compressor
2.4.4 Measure of success
Accounting for the iron losses allow us to achieve up to 20% power losses reduction on the tested 1HP induction motor. This is achieved by advanced control techniques
The efficiency increases accordingly up to 3%. This reduces sensibly the stress on the motor and increases the machine rating
Control techniques for compressor reduce the risk of instabilities during the operations. This is achieved through combined backstepping control and Model Predictive Control techniques. Recycle valves, usually present in modern plants, can be successfully considered.
2.5 WP5: Electricity optimization
2.5.1 Objectives
WP5 aims to deliver technology prototypes for energy/cost savings at the scale of production processes:
* By analysing the interaction between large and variable industrial loads and the electrical grid
* By developing and implementing scheduling optimization methods to integrate the production of a steel plant.
* By developing a framework and methods for demand response of energy-intensive processes
Implementing a practical case study of the control algorithms by:
2.5.2 Intended outcome
Create the means to deal with increasing volatility in production, energy and raw material availability. The key aspect is to ensure seamless integration and availability of data and information across the plant, enabling user interaction and connection to optimization solutions.
Bridge the gap between production, energy management and maintenance. This can be enabled through intelligent and adaptable advanced analysis tools.
Enable energy and cost savings through optimization. This will require new approaches where traditional planning models must be expanded to cover larger problem instances and energy consumption (industrial demand-side management).
2.5.3 Methodology developed
Level-2 systems have been integrated and all relevant information can be collected and visualized. Flexible user interfaces linked to optimization enable the possibility to perform planning tasks manually, partly or fully supported by the optimization tools.
Data mining applying statistical pattern recognition and signal processing has made it possible to identify maintenance needs more efficiently.
Mixed integer linear programming optimization models have been studied to include production scheduling and energy volatility, i.e. availability and price. Continuous and discrete-time scheduling models using various decomposition schemes have been developed, for instance applying mean value cross decomposition.
2.5.4 Measure of success
The use of scheduling optimization improved the coordination between different production stages in the melt shop of a stainless steel plant, lowered the hold-up times and increased the production rate.
The potential benefits have been estimated to be:
* 5% reduction in energy consumption,
* 4-5% in lead times,
* 2-3% in inventory levels.
The potential impact of intelligent energy-aware scheduling of processes has been evaluated to enable a reduction of electricity cost of 2-20%.
The use of an optimized coordination policy between melt shop and rolling mill reduce thermal losses. The anticipated increase of hot charging ratio is up to +22%
3 IMPACT
3.1 OVERVIEW
ENERGY-SMARTOPS is an FP7 Marie Curie Initial Training Network (ITN) project. The Early-Stage Researchers recruited are known as “Marie Curie Fellows”. The quote below is description of the ITN scheme from the FP7 People Work programme:
“This action aims to improve career perspectives of early-stage researchers in both public and private sectors, thereby making research careers more attractive to young people. This will be achieved through a trans-national networking mechanism, aimed at structuring the existing high-quality initial research training capacity throughout Member States and associated countries. Direct or indirect involvement of organisations from different sectors, including (lead-) participation by private enterprises in appropriate fields, is considered essential in the action. In particular, the action aims to add to the employability of the recruited researchers through exposure to both academia and enterprise, thus extending the traditional academic research training setting and eliminating cultural and other barriers to mobility.”
This section aims to show how the ENERGY-SMARTOPS project has delivered the impacts expected from an ITN project.
3.2 SOCIOECONOMIC IMPACT
3.2.1 Impact on careers and skills:
The support of the ITN scheme for the project, especially with its high level of industrial engagement, had a demonstrated impact on the careers of its Early Stage Researchers. Direct evidence is that ten of the 16 ESRs have continued further employment with their host organizations. Four others have moved into excellent jobs involving a different sector (one has moved from university to industry, one moved from industry to become a university lecturer, one is a self-employed entrepreneur running his own business, and one has moved to a different industry sector). The two remaining ESRs are currently job seeking while finishing their PhDs.
The researchers have had many benefits from their Marie Curie Fellowships including:
*Access to quality of training and research supervision and career development
*Access to high quality research facilities, laboratories and industrial equipment
*A solid preparation on the primary subjects of research to doctoral level
*Research outputs especially publications and patents
*Development of complementary skills including team working, leadership, and project management
*International mobility experience, both when taking up their appointments and during secondments
*Intersectoral experience by working with ERS from the other sector during secondments
*Forging a strong professional network with other ESRs and the managers/supervisors, which will continue after the project
3.2.2 Strengthening European innovation capacity:
The ENERGY-SMARATOPS partners understood the need to avoid fragmentation and duplication of work in different organizations and selected the ITN scheme as a programme to bring together researchers with similar interests and complementary activities. Its activities strengthen European innovation capacity by means of:
* Increasing the competitiveness of European companies and contributing to the Europe 2020 strategy for smart, sustainable and inclusive growth
->using state-of-the-art knowledge from academia through planned secondments and the knowledge base built during the lifetime of the ITN to support the development of new technologies, tools and methodologies;
->developing mutual trust between universities and the industry, leading to openness in sharing the research challenges and case studies;
->taking research ideas from the laboratory for rapid prototyping and testing in industrial partners up to TRL 5, in readiness for larger scale demonstration at higher TRL levels.
*Training a cohort of researchers who can move comfortably between academia and industry
->education of future scientific leaders in Europe with a strong academic background;
->giving the ESRs experience of generating creative and potentially innovative ideas;
->enhancing the teaching activities of universities through involvement of industry.
*Making strategic extensions to existing academic and industrial collaborations, thus strengthening a Europe-wide research and development capability.
The project enabled local activity to operate at a higher level as a European-wide initiative enabling individual research outputs to have a synoptic and coordinated impact. A public web-based dissemination site has captured and collected together the findings and experiences of the project, descriptions of the ESRs and a list of published papers.
3.2.3 Lasting R&D collaboration:
The ENERGY-SMARTOPS project has established new long-term collaborations and friendships between the Consortium Partners which enabled them to do substantial pieces of work together. Many of the partners are continuing their collaborations after the end of the project. A specific future plan is to propose a new ITN to explore the exciting new ideas concerning condition-based maintenance that have emerged from ENERGY-SMARTOPS.
3.2.4 Impact on European industrial energy efficiency:
ENERGY-SMARTOPS addressed the European process industries, one of the largest and most dynamic industrial sectors in the EU. In particular, it targeted the efficient and sustainable operation of the installed assets of the process industries. The research tasks align with well with topics identified in several EU roadmaps [1]-[3].
[1] European Commission, 2011, A resource-efficient Europe – Flagship initiative under the Europe 2020 Strategy, Communication from the Commission to the European Parliament, http://ec.europa.eu/resource-efficient-europe/pdf/resource_efficient_europe_en.pdf
[2] SPIRE, 2013, Sustainable Process Industry, Multi-Annual Roadmap, pub: European Commission Directorate G Industrial Technologies, Brussels.
[3] ProcessIT, 2013, European Roadmap for Industrial Process Automation, Ed: P. Lingman, pub: Artemis Industry Association, Eindhoven, The Netherlands.
The ENERGY-SMARTOPS project has made contributions to the energy efficiency of European industry, as outlined in the Outcomes and New Knowledge section above. As well as a major impact on the state-of-the art by means of publications, the intersectorial secondments have facilitated technology transfer because the partners include companies that produce products (the end-users of technology), the universities, and the R&D organizations of the technology companies who have the capacity to develop and commercialize research outputs and turn them into products. At the end of the project, there are more than 70 published papers and three patent applications arising from the work of the ESRs. There are, additionally, several significant new insights into how energy savings can be gained in the process industries.
3.2.5 Gender aspects:
It is generally not possible in a project involving R&D industrial and automation engineers to achieve an equal balance between men and women, because the pool of male candidates who meet the essential qualifications is larger than the pool of qualified female candidates. Nevertheless, three of the ten partner organizations had female Scientists-in-charge, including the coordinator at Imperial who is female. Two of the 16 ESRs were female.
All partners are committed to providing a positive and inclusive working environment in order to bring out the full potential of all staff. The consortium partners were aware of the targets and provisions of the Commission Staff working document Sec_2005_379 Women and Science: Excellence and Innovation – Gender Equality in Science.