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Phd presentation

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Phd presentation, you can find also the full version of my thesis.

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Phd presentation

  1. 1. Ph.D. student: Giulio Vialetto Supervisor: Prof. Marco Noro ENERGY EFFICIENCY INTO INDUSTRIAL FACILITIES
  2. 2. PREFACE The aim of the research activity was to improve the efficiency on energy generation in industrial facilities by using both innovative energy systems (“hardware”) and big data methods (“software”). The idea is that if these improvements are adopted at the same time, efficiency would be higher compared to the case they are adopted separately. An energy system should improve both on generation both on operation strategy.
  3. 3. ENERGY EFFICIENCY INTO INDUSTRIAL FACILITIES SOFC – Air Source Heat pump (ASHP) system for advanced heat recovery
  4. 4. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua SOFC (solid oxide fuel cell) converts fuel into electricity and heat with high efficiency. Heat is recovered from waste gases that have a high percentage of water (steam). If not only sensible but also latent heat can be recovered, energy efficiency of the system is increased. Air source heat pumps (ASHP) are cheaper than ground source heat pumps (GSHP). In some climates, however, evaporation section may freeze. SOFC, ASHP – AN OVERVIEW
  5. 5. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua SOFC waste gases are mixed with inlet air into an adiabatic mixer, increasing both temperature and absolute humidity. The aim is to increase COP of ASHP and decrease the freezing of evaporation section. SOFC – ASHP INTEGRATED SYSTEM
  6. 6. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Simulations were performed with a 50 kW nominal power SOFC and an ASHP with 7.7 kW nominal heating capacity. Air inlet temperature varies from –7.5 °C to 15 °C, relative humidity from 25% to 100%. Two benchmarks are defined to evaluate the performances: COP variation and %PES. COP variation verifies if COP of the system proposed is higher than a traditional ASHP. %PES verifies which is the primary energy saving of the innovative system compared with a traditional one. SIMULATION PARAMETERS AND BENCHMARKS 𝐶𝑂𝑃𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛 = 𝐶𝑂𝑃𝑖𝑛𝑛𝑜𝑣 ,𝑠𝑦𝑠 𝐶𝑂𝑃𝑡𝑟𝑎𝑑 ,𝑠𝑦𝑠 − 1 ∙ 100 %𝑃𝐸𝑆 = 1 − 𝑃𝐸𝑖𝑛𝑛𝑜 ,𝑠𝑦𝑠 𝑃𝐸𝑡𝑟𝑎𝑑 ,𝑠𝑦𝑠 ∙ 100 = 1 − 𝐸𝑎𝑣𝑎 𝜂 𝑒𝑙𝑒 + 𝐻𝑎𝑣𝑎 𝜂 𝑏𝑜𝑖𝑙𝑒𝑟 𝐹𝑆𝑂𝐹𝐶 ∙ 100
  7. 7. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua COP variation varying the external inlet air temperature for four very different cases in terms of SOFC nominal power and air relative humidity. RESULTS - COP VARIATION
  8. 8. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Primary energy saving varying the external inlet air temperature for four very different cases in terms of SOFC nominal power and air relative humidity. RESULTS - %PES
  9. 9. Polygeneration system – Hydrogen production with RSOC ALTERNATIVE ENERGY GENERATION SYSTEM FOR INDUSTRIAL FACILITY
  10. 10. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua A reversible solid oxide cells (RSOC) system could work as solid oxide fuel cells (SOFC) producing energy (electricity and heat at high temperature) or as electrolyser (solid oxide electrolyser cells, SOEC) where heat and electricity are used to produce hydrogen. It is proposed that a combined system composed by some sub-systems working as SOFC and some as SOEC creates a reversible energy system where is possible to vary H/P ratio having hydrogen as sub product. RSOC – AN INTRODUCTION RSOC HE G
  11. 11. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Varying the ratio between RSOC working as SOFC and SOEC (nRSOC), heat to power ratio varies too. It could cover the range of the other cogeneration technologies. RSOC - HEAT TO POWER VARIATION 𝑛 𝑅𝑆𝑂𝐶 = 𝑃𝑆𝑂𝐸𝐶 𝑃𝑆𝑂𝐹𝐶 𝐻 𝑃 𝑅𝑆𝑂𝐶 = 𝐻 𝑃 𝑆𝑂𝐹𝐶 − 𝐻 𝑃 𝑆𝑂𝐸𝐶 ∗ 𝑛 𝑅𝑆𝑂𝐶 1 − 𝑛 𝑅𝑆𝑂𝐶 𝑃𝑆𝑂𝐹𝐶 = 1 1 − 𝑛 𝑅𝑆𝑂𝐶 ∗ 𝑃𝑅𝑆𝑂𝐶 𝑃𝑆𝑂𝐸𝐶 = 𝑛 𝑅𝑆𝑂𝐶 1 − 𝑛 𝑅𝑆𝑂𝐶 ∗ 𝑃𝑅𝑆𝑂𝐶
  12. 12. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Paper production is an intensive energy consumption and it requires both electricity and heat. A paper mill asked to analyse its energy generation system to improve efficiency. While working on operation data it was decided to propose an alternative energy generation system: RSOC are proposed to improve energy production and, when production rate is low, to produce hydrogen. The farm has two production lines, it could work only Line 1 (Case 1), only Line 2 (Case 2) or both of the lines (Case 1+2). Energy consumption and also heat to power ratio vary depending on the lines working. CASE STUDY – AN OVERVIEW
  13. 13. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua The traditional energy system (left) is improved by RSOC (right). One of the two steam turbines (the oldest part of the system, installed in the ‘60) could be dismissed. ENERGY SYSTEM IMPROVEMENT PROPOSED
  14. 14. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Adoption of RSOC could increase efficiency on energy generation: it is estimated that if all of the production lines work (Case 1+2), it is possible to achieve a primary energy saving (PES) of 6.5% without the production of hydrogen. Meanwhile, if only line 1 (Case 1) or line 2 (Case 2) works, hydrogen is produced with a flow rate of 16.14-16.86 kg/h, a PES of 2% on energy production and a PES of 45% on hydrogen production can be reached. THERMODYNAMIC ANALYSIS CASE H2 PROD. PES EN. GEN. PES H2 gen Case 1 16.857 kg/h 2.67% 45.62% Case 2 16.137 kg/h 2.27% 45.28% Case 1+2 - 6.54% -
  15. 15. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua The aim of the system is not only to increase efficiency but also to produce hydrogen with a lower cost compared to other technology. A sensitive analysis on RSOC purchase cost varying it between - 10% and 30% show that H2 cost varies between 6-8 €/kg (whereas the costs is 10 €/kg if it is produced by using Proton Exchange Membrane Electrolyser (PEMEC)). HYDROGEN COST
  16. 16. Clustering to improve energy system BIG DATA ANALYSIS FOR ENERGY EFFICIENCY
  17. 17. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Meanwhile more data on energy demands are available, energy system are still analysed using cumulative curve of consumption. In a case that two types of energy (for example heat and electricity) are consumed, it is unknown which correlations there are between them. (Figure taken from A. Biglia, F. V. Caredda, E. Fabrizio, M. Filippi, and N. Mandas, “Technical-economic feasibility of CHP systems in large hospitals through the Energy Hub method: The case of Cagliari AOB,” Energy Build., vol. 147, pp. 101–112, Jul. 2017) SIZING COGENERATION SYSTEM – AN OVERVIEW
  18. 18. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua It is proposed to use cluster analysis to perform clustering on energy data demands. The main scope is to divide the observed data into homogenous groups and use them to design and size an energy system. CLUSTERING – AN INTRODUCTION
  19. 19. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Two different analyses based on clustering are proposed: • Power analysis, every observation is considered separately to define clusters with similar values of the variables (i.e. electricity demand and H/P ratio). This information, and how such variables vary inside the cluster, will suggest the most suitable polygeneration technology and/or information to design the generation system; • Profile analysis, daily energy demand profile (not a single observation) is defined and clustered to identify how energy demand varies during daytime. Possible mismatching can be detected between energy demand and energy production using energy system defined with Power analysis. CLUSTERING AND ENERGY DATA – PROPOSED ANALYSES
  20. 20. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua A workflow is then proposed to perform cluster analysis both for power and profile analysis. Data cleaning is necessary to clean dataset from missing and/or bad measurement records. A MATLAB script combined with Machine Learning toolbox was defined to perform Power and Profile analyses. ANALYSIS WORKFLOW • Import dataset • Data validation and cleaning DATASET • Application of silhouette criteria to define number of cluster DEFINE HYPERPARAMETERS • Clustering with K- Means CLUSTERING • Definition of cluster average curves AVERAGE CURVES
  21. 21. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua A case study is proposed concerning an industrial facility selling wood (timber) window laminated, plywood, engineered veneer, laminate, flooring and white wood. The industrial process requires to dry wood into kilns, and to store it into warehouses. Electricity is used for the production equipment, offices, lighting purpose into the warehouses, and to charge electric forklifts. Heat is used to produce steam for the kilns that work at about 70 °C. CASE STUDY – INTRODUCTION
  22. 22. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua CASE STUDY - POWER ANALYSIS
  23. 23. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Cluster Number of observations 1 31.91 % 2 21.90 % 3 0.27 % 4 45.92 % CASE STUDY – PROFILE ANALYSIS
  24. 24. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua On the dataset both power and profile analyses are performed. Firstly power analysis suggests the most suitable cogeneration system – micro gas turbines. Profile analysis gives also useful information to define operation strategy and energy storage (in this case heat). CASE STUDY – PROPOSED IMPROVEMENTS
  25. 25. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Two different TO BE scenarios are proposed to improve efficiency on energy generation. First, an improvement only on energy generation (microturbines) is proposed with heat storage. CASE STUDY – SCENARIO TO BE 1
  26. 26. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua In scenario TO BE 2 operation strategy is improved, cogeneration stops when heat storage is not able to store more heat: the aim is to avoid heat losses. CASE STUDY – SCENARIO TO BE 2
  27. 27. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Analysis on primary energy saving (PES) between AS IS and TO BE scenarios is then performed. It is possible to appreciate that saving of 6 % can be achieved. Heat storage is important to achieve this goal: the mean heat stored level is close to 50 % covering between 4 - 5 % on total heat demand (IC). CASE STUDY – BENCHMARK Scenario Primary energy Saving AS IS 6.505 GWh - TO BE 1 6.377 GWh 2.01 % TO BE 2 6.137 GWh 6.00 % 𝑃𝐸 = 𝐹 + 𝐸 𝑔𝑟𝑖𝑑,𝑖𝑛 − 𝐸 𝑔𝑟𝑖𝑑,𝑜𝑢𝑡 0.434 𝐼𝑆 = 𝐻𝑠𝑡𝑜𝑟𝑒𝑑,𝑖𝑛 𝐻 𝐶𝐻𝑃 𝐼 𝐶 = 𝐻𝑠𝑡𝑜𝑟𝑒𝑑,𝑜𝑢𝑡 𝐻 𝑢𝑠𝑒𝑟 Scenario IS IC % Mean heat stored TO BE 1 4.6 % 4.3 % 50.5 % TO BE 2 5.7 % 4.7 % 48.9 %
  28. 28. Clustering and kNN for short-term forecasting BIG DATA ANALYSIS FOR ENERGY EFFICIENCY
  29. 29. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Clustering is proposed not only to design energy system but also to increase their efficiency forecasting energy consumption data. Clustering is proposed to find similar patterns of consumption and, consequently, average patterns of consumption. These (average) patterns are then used to forecast consumption using k-Nearest Neighbour (kNN) machine learning method. CLUSTERING FOR FORECASTING – AN OVERVIEW • Observation dataset trains the model MODEL TRAINING • Observations are used to classify the correspondent average curve CURVE CLASSIFICATION • Average curve is used to define forecast FORECAST
  30. 30. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua A workflow is defined to train the model and choose its parameters (hyperparameters). Novelties are also proposed on dataset normalisation method and hyperparameter definition. Both of the workflows are implemented with a MATLAB script using Machine Learning toolbox. FORECASTING WORKFLOW • Definition and normalisation • Define validation, training and test dataset DEFINE DATASET • Define hyper parameters of clustering and kNN using validation dataset DEFINE HYPER PARAMETERS • Define clusters using training dataset TRAIN CLUSTER MODEL • Define kNN model using training dataset TRAIN kNN MODEL • Verify model using test dataset TEST MODEL
  31. 31. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Firstly instead of normal score, a percentage norm is proposed. For each observation, average is calculated and then used to normalise observation. It is expected that this method decreases only scale effect on dataset. DATA NORMALISATION
  32. 32. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Mean absolute percentage error (MAPE) is then proposed to define the optimum number of cluster to divide the dataset. This method is useful to predict which would be the error on forecasting. Number of cluster (n) could be defined as: MAPE CRITERIA FOR HYPERPARAMETER DEFINITION min(n) | MAPE(n) < (MAPE(n+1)+MAPE(n+2)+MAPE(n+3))/3min(n) | MAPE(n) < MAPE_limit
  33. 33. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Dataset previously used for the previous analysis was used also to test the proposed forecast method. Firstly, it is possible to appreciate that MAPE criteria was able to predict error on forecast when training and test is performed. It is possible to appreciate that forecast error in some cases is about 3.5 %. CASE STUDY - INTRODUCTION
  34. 34. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Novelties proposed on normalisation (percentage norm) decreases MAPE error compared to standard score. IMPROVEMENT ON DATA NORMALISATION
  35. 35. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Performance on electricity (on top) and on heat (on bottom) demand forecast varying observed demand (supp ort) and forecasted values (forecast). CLUSTERING FOR FORECASTING
  36. 36. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua MAPE between validation dataset and test dataset. Validation dataset is able to predict MAPE error on test dataset MAPE BETWEEN VALIDATION AND TEST DATASET Curve Energy Validation dataset Test dataset MAPE MAPE1 MAPE RMSE1 RMSE 8-4 Electricity 3.60% 2.75% 3.58% 5.15 3.82 8-4 Heat 35.41% 32.95% 34.11% 93.43 55.43 10-4 Electricity 3.71% 2.74% 3.57% 5.15 3.82 10-4 Heat 35.23% 32.7% 34.95% 93.2 54.82 10-8 Electricity 4.79% 2.9% 4.47% 5.47 3.53 10-8 Heat 36.66% 35.3% 34.12% 90.03 41.99 12-8 Electricity 4.69% 2.8% 4.47% 5.31 3.53 12-8 Heat 39 % 32.1% 37.21% 95.14 43.05 .
  37. 37. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua In this thesis improvements on energy generation both using SOFC/SOEC both data analytics are proposed. In the first part innovation on SOFC are proposed to increase efficiency on energy generation. A novel heat recovery for system composed by ASHP and SOFC is proposed and analysed. Simulations show that it is possible to increase efficiency of the system, COP is higher when a powerful SOFC is available and when air has a high relative humidity. Then Reversible solid oxide cells (RSOC) are proposed as flexible energy system where it is possible to vary H/P ratio by modifying the sub-systems working as SOFC and as SOEC. Hydrogen is produced as sub-product. RSOC is proposed to improve energy generation into an industrial facility (paper mill) to dismiss an old steam turbine. Primary energy saving occurs varying between 2.27 % - 6.5 %. Hydrogen could be produced with a rate of 16 kg/h with a lower cost compared to traditional electrolyser such as PEMEC. CONCLUSION
  38. 38. OVERVIEW METHOD SIMULATION CONCLUSION Energy efficiency into industrial facilities – Ph.D. student Ing. Giulio Vialetto, supervisor Prof. Marco Noro Doctoral School of Industrial Engineering, Curriculum Energetic Engineering - XXXII Cycle – University of Padua Data analytics then proposed to improve efficiency using energy demand data. Clustering is used to divide dataset into homogenous groups to define which is the most suitable energy generation technology with power analysis, profile analysis is then used to check if energy storage occurs and/or which is the most suitable operation strategy. Proposed methodology is then applied to an industrial case study to enhance its energy cogeneration system. It was demonstrated that a PES of 6 % can be achieve improving energy generation. Clustering combined with kNN are proposed also to perform short-term forecast of energy demand. Novelties are proposed on data normalisation to increase accuracy on forecasting. Method proposed was then tested with a case study, MAPE on electricity forecasting was 3.6 %. Consumption forecasting could be used to improve control on generation, to decrease energy production when it is unnecessary. CONCLUSION
  39. 39. PUBLISHED PAPERS Co-Authors Journal Title Vialetto Giulio, Noro Marco Energy Conversion and Management (under review) An innovative approach to design cogeneration systems based on big data analysis and use of clustering Vialetto Giulio, Noro Marco Energies 2019, 12(23), 4407 Short forecasting method based on clustering and kNN: application to an industrial facility powered by a cogenerator Vialetto Giulio, Noro Marco Proceedings “14th SDEWES Conference”, Dubrovnik, 2019 An innovative approach to design cogeneration systems based on big data analysis and use of clustering Vialetto Giulio, Noro Marco, Colbertaldo , Rokni Masoud International Journal of Hydrogen Energy, 2019, 44(19), pp. 9608-9620 Enhancement of energy generation efficiency in industrial facilities by SOFC – SOEC systems with additional hydrogen production Vialetto Giulio, Noro Marco, Rokni Masoud Journal of Electrochemical Energy Conversion and Storage, 2019, 16(2), 021005, Paper No: JEECS-18-1064 Studying a hybrid system based on solid oxide fuel cell combined with an air source heat pump and with a novel heat recovery Vialetto Giulio, Noro Marco, Rokni Masoud Proceedings “12th SDEWES Conference”, Dubrovnik, 2017, SDEWES2017.75, ISSN 1847-7178 Analysis of a cogeneration system based on solid oxide fuel cell and air source heat pump with novel heat recovery Vialetto Giulio, Noro Marco, Rokni Masoud Journal of Sustainable Development of Energy, Water and Environment Systems, 2017, 5(4), pp. 590-607 Thermodynamic Investigation of a Shared Cogeneration System with Electrical Cars for Northern Europe Climate Vialetto Giulio, Noro Marco, Rokni Masoud International Journal of Hydrogen Energy, 2017 42(15), pp. 10285-10297 Combined micro-cogeneration and electric vehicle system for household application: An energy and economic analysis in a Northern European climate
  40. 40. INDUSTRIAL SUPPORTER I would like to thank Mosaico S.r.L. (part of BURGO Group S.p.A.) and Corà S.p.A. that provided useful case study for the methods proposed.
  41. 41. THANK YOU FOR YOUR ATTENTION ANY QUESTION?

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