D. Anagnostakis, J.M. Ritchie and T. Lim explore how Lanner predictive simulation software WITNESS can help improve the environmental impact of a manufacturing system.
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Presenting at the Lanner predictive simulation conference, 2016, D. Anagnostakis, J.M. Ritchie and T. Lim explore how Lanner predictive simulation software WITNESS can help improve the environmental impact of a manufacturing system.
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D. Anagnostakis, J.M. Ritchie and T. Lim explore how Lanner predictive simulation software WITNESS can help improve the environmental impact of a manufacturing system.
- 1. Predictive Simulation Conference April 28, 2016 MTC, Coventry, UK Modelling and Improving the Environmental Impact of a Manufacturing System D. Anagnostakis, J. M. Ritchie, T. Lim
- 2. Outline 1. Introduction 2. Case study 3. Manufacturing system 4. Modelling 5. Simulation 6. Results and discussion 7. Conclusion
- 3. Introduction The company Progress Rail Services (UK) Ltd. • Design and manufacture railway switches and crossings. • Crossing manufacture at South Queensferry plant. • Material: austenitic manganese steel. • Energy and carbon reduction pressures. 1. Introduction 2. Case study 3. Manufacturing system 4. Modelling 5. Simulation 6. Results and discussion 7. Conclusion
- 4. Case Study Aim Environmental impact assessment of a production system within a manufacturing company. • Environmental performance indicators regarding energy consumption & carbon emissions. • Discrete event simulation models using WITNESS predictive simulation software (Lanner Ltd., UK). 1. Introduction 2. Case study 3. Manufacturing system 4. Modelling 5. Simulation 6. Results and discussion 7. Conclusion
- 5. Manufacturing system Casting Heat Treatment Machine Shop Finishing department Production of 10 crossing variants Similar geometry Different length and width Manufacturing System 1. Introduction 2. Case study 3. Manufacturing system 4. Modelling 5. Simulation 6. Results and discussion 7. Conclusion
- 6. Heat Treatment Charger: Loading 1 1 2 3 Furnace: Heating 2 1 Quenching Tank: Cooling 3 1 Overhead Crane Overhead Crane Storage1 Charger: Unloading 1
- 7. Forklift Machine Shop Press Machine Flattening bent crossings 1 2x Milling Machine Top and Bottom surfacing 2 1 2 3x CNC Milling Machine Geometry 3 Storage Storage 2x Overhead Crane Forklift 3 2 3 3 Overhead Crane Line 2 Line 1
- 8. Finishing Storage Overhead Crane Forklift 3x Finishing workstations Removing imperfections 1 1 1 1
- 9. Modelling • Product demand • Power required • Resources Input • Machines • Setup time • Process time System • Energy consumption • Carbon emissions Output 1. Introduction 2. Case study 3. Manufacturing system 4. Modelling 5. Simulation 6. Results and discussion 7. Conclusion Production launch Peak demand End of production
- 10. Power consumption modelling 1. Cranes and Charger Main hoisting and lowering power: 𝑃ℎ = 𝑊𝑙𝑖𝑓𝑡 + 𝑊𝑝𝑎𝑟𝑡 ∗ 𝑔 ∗ 𝑣ℎ 60∗𝑒𝑓𝑓 𝑃𝑙 = −(𝑊𝑙𝑖𝑓𝑡 + 𝑊𝑝𝑎𝑟𝑡) ∗ 𝑔 ∗ 𝑣 𝑙 60 ∗ 𝑒𝑓𝑓 Main travelling power: 𝑃𝑡𝑟 = 𝑊𝑙𝑖𝑓𝑡 + 𝑊𝑝𝑎𝑟𝑡 ∗ 𝑐 ∗ 𝑔 ∗ 𝑣 𝑡𝑟 60∗𝑒𝑓𝑓 Power for acceleration and deceleration in hoisting motion: 𝑃ℎ,𝑎𝑐𝑐 = (𝑊𝑙𝑖𝑓𝑡 + 𝑊𝑝𝑎𝑟𝑡) ∗ (𝑣ℎ 60 2 𝑡 𝑎𝑐𝑐∗𝑒𝑓𝑓 𝑃ℎ,𝑑𝑒𝑐 = − 𝑊𝑙𝑖𝑓𝑡 + 𝑊𝑝𝑎𝑟𝑡 ∗ (𝑣ℎ 60 2 𝑡 𝑑𝑒𝑐 ∗ 𝑒𝑓𝑓 Power for acceleration and deceleration in lowering motion 𝑃𝑙,𝑎𝑐𝑐 = 𝑊𝑙𝑖𝑓𝑡 + 𝑊𝑝𝑎𝑟𝑡 ∗ (𝑣 𝑙 60 2 𝑡 𝑎𝑐𝑐 ∗ 𝑒𝑓𝑓 𝑃𝑙,𝑑𝑒𝑐 = − 𝑊𝑙𝑖𝑓𝑡 + 𝑊𝑝𝑎𝑟𝑡 ∗ (𝑣 𝑙 60 2 𝑡 𝑑𝑒𝑐∗𝑒𝑓𝑓 Power for motor acceleration and deceleration in hoisting 𝑃ℎ,𝑚,𝑎𝑐𝑐 = 𝑊𝐾𝑟 2 ∗ (2∗𝜋∗𝑛 𝑚 60 2 1000∗𝑡 𝑎𝑐𝑐 𝑃ℎ,𝑚,𝑑𝑒𝑐 = −𝑊𝐾𝑟 2 ∗ (2∗𝜋∗𝑛 𝑚 60 2 1000∗𝑡 𝑑𝑒𝑐 Power for motor acceleration and deceleration in lowering 𝑃𝑙,𝑚,𝑎𝑐𝑐 = 𝑊𝐾𝑟 2 ∗ 2∗𝜋∗ 𝑣 𝑙 𝑣ℎ ∗𝑛 𝑚 60 2 1000∗𝑡 𝑎𝑐𝑐 𝑃𝑙,𝑚,𝑑𝑒𝑐 = −𝑊𝐾𝑟 2 ∗ 2∗𝜋∗ 𝑣 𝑙 𝑣ℎ ∗𝑛 𝑚 60 2 1000∗𝑡 𝑑𝑒𝑐 Power for acceleration and deceleration in travelling. 𝑃𝑡𝑟,𝑎𝑐𝑐 = 𝑊𝑙𝑖𝑓𝑡 + 𝑊𝑝𝑎𝑟𝑡 ∗ 𝑣 𝑡𝑟 60 2 𝑡 𝑎𝑐𝑐∗𝑒𝑓𝑓 𝑃𝑡𝑟,𝑑𝑒𝑐 = − 𝑊𝑙𝑖𝑓𝑡 + 𝑊𝑝𝑎𝑟𝑡 ∗ ( 𝑣 𝑡𝑟 60 )2∗𝑒𝑓𝑓 𝑡 𝑑𝑒𝑐 Power for motor acceleration and deceleration in travelling 𝑃𝑡𝑟,𝑚,𝑎𝑐𝑐 = 𝑊𝐾𝑟 2 ∗ (2∗𝜋∗𝑛 𝑚 60 2 1000∗𝑡 𝑎𝑐𝑐 𝑃𝑡𝑟,𝑚,𝑑𝑒𝑐 = −𝑊𝐾𝑟 2 ∗ (2∗𝜋∗𝑛 𝑚 60 2 1000∗𝑡 𝑑𝑒𝑐
- 11. Power consumption modelling 2. Heat Treatment furnace Preheating: Output energy: 𝑄 𝑝𝑟𝑒,𝑜𝑢𝑡 = 𝑚 𝑎𝑖𝑟 ∗ 𝐶 𝑝,𝑎𝑖𝑟 ∗ 𝛥𝑇1 Output power: 𝑃𝑝𝑟𝑒,𝑜𝑢𝑡 = 𝑄 𝑝𝑟𝑒,𝑜𝑢𝑡 𝑡 𝑝𝑟𝑒 Input power: 𝑃𝑝𝑟𝑒,𝑖𝑛 = 𝑃 𝑝𝑟𝑒,𝑜𝑢𝑡 𝑒𝑓𝑓 𝑓𝑢𝑟𝑛𝑎𝑐𝑒 Regular heating: Output energy: 𝑄 𝑟𝑒𝑔,𝑜𝑢𝑡 = 𝑀𝑐𝑟𝑜𝑠𝑠 ∗ 𝐶 𝑝,𝑠𝑡𝑒𝑒𝑙 ∗ 𝛥𝑇2 Output power: 𝑃𝑟𝑒𝑔,𝑜𝑢𝑡 = 𝑄 𝑟𝑒𝑔,𝑜𝑢𝑡 𝑡 𝑟𝑒𝑔 Input power: 𝑃𝑟𝑒𝑔,𝑖𝑛 = 𝑃𝑟𝑒𝑔,𝑜𝑢𝑡 𝑒𝑓𝑓 𝑓𝑢𝑟𝑛𝑎𝑐𝑒 3. Quenching tank Agitators’ electrical motors: Input power: 𝑃𝑎𝑔𝑖𝑡,𝑖𝑛 = 6 ∗ 𝑂𝑢𝑡𝑝𝑢𝑡 𝑝𝑜𝑤𝑒𝑟 𝑒𝑓𝑓 𝑎𝑔𝑖𝑡∗𝑒𝑓𝑓𝑎,𝑚𝑜𝑡 Circulation system: Input power: 𝑃𝑝𝑢𝑚𝑝,𝑖𝑛 = 2 ∗ 𝑂𝑢𝑡𝑝𝑢𝑡 𝑝𝑜𝑤𝑒𝑟 𝑒𝑓𝑓𝑝𝑢𝑚𝑝∗𝑒𝑓𝑓𝑝,𝑚𝑜𝑡 Cooling Unit system: Input power: 𝑃𝑓𝑎𝑛,𝑖𝑛 = 𝑂𝑢𝑡𝑝𝑢𝑡 𝑝𝑜𝑤𝑒𝑟 𝑒𝑓𝑓 𝑓𝑎𝑛∗𝑒𝑓𝑓 𝑓,𝑚𝑜𝑡𝑜𝑟 4. Hydraulic Press machine Advancing process: Advancing output power: 𝑃𝑜𝑢𝑡,𝑎𝑑𝑣 = 𝐹𝑎𝑑𝑣 ∗ 𝑣 𝑎𝑑𝑣 Advancing input power: 𝑃𝑖𝑛,𝑎𝑑𝑣 = 𝑃 𝑜𝑢𝑡,𝑎𝑑𝑣 𝑒𝑓𝑓 𝑚𝑜𝑡∗𝑒𝑓𝑓𝑝𝑢𝑚𝑝 Retracting process: Retracting output power: 𝑃𝑜𝑢𝑡,𝑟𝑒𝑡𝑟 = 𝐹𝑟𝑒𝑡𝑟 ∗ 𝑣 𝑟𝑒𝑡𝑟 Retracting input power: 𝑃𝑖𝑛,𝑟𝑒𝑡𝑟 = 𝑃 𝑜𝑢𝑡,𝑟𝑒𝑡𝑟 𝑒𝑓𝑓 𝑚𝑜𝑡∗𝑒𝑓𝑓𝑝𝑢𝑚𝑝 5. Machining processes Material Removal Rate (MRR): 𝑀𝑅𝑅 = 𝑑𝑒𝑝𝑡ℎ 𝑐𝑢𝑡 ∗ 𝑤𝑖𝑑𝑡ℎ 𝑐𝑢𝑡 ∗ 𝑓𝑒𝑒𝑑 𝑟𝑎𝑡𝑒 Required input power: 𝑃𝑖𝑛,𝑐𝑢𝑡 = 𝑀𝑅𝑅 ∗𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝐶𝑢𝑡𝑡𝑖𝑛𝑔 𝐸𝑛𝑒𝑟𝑔𝑦 𝑒𝑓𝑓𝑠𝑝,𝑚𝑜𝑡 6. Others Hand held grinders: Input power: 𝑃𝑖𝑛,𝑔𝑟 = 𝑂𝑢𝑡𝑝𝑢𝑡 𝑝𝑜𝑤𝑒𝑟 𝑒𝑓𝑓𝑔𝑟,𝑚𝑜𝑡 Forklifts: The forklift vehicles have a lifting capacity 10 ton and consume diesel fuel. From the specification provided the typical forklift’s fuel consumption is 7 lit/hour.
- 12. Heat Treatment Machine Shop Finishing Modelling: Witness Model
- 13. 1 2 3 Heat Treatment Machine shop Finishing Final WITNESS Model Modelling components in WITNESS WITNESS - Part route Summary WITNESS - Part file and Input structure WITNESS - Usage details report WITNESS - Built-in graphs WITNESS - Output variables
- 14. Simulation Scenario • 240 working days • 3 shifts x 7.5 working hours/shift • Annual production volume: 800 crossings • Products demand and variants: 1. Introduction 2. Case study 3. Manufacturing system 4. Modelling 5. Simulation 6. Results and discussion 7. Conclusion
- 15. Simulation
- 16. Results and discussion Current plant: Total energy consumption: 722.5 MWh Natural Gas provides 60% of total consumed energy Total carbon emissions: 206 Tones CO2 Electricity causes 50% of total CO2 emissions 0 200 400 600 800 EnergyUsage(MWh) Electricity Natural Gas Diesel fuel Total Energy Usage 0 50 100 150 200 250 TotalEmissions(Ton.CO2) Electricity Natural Gas Diesel fuel Total Emissions 1. Introduction 2. Case study 3. Manufacturing system 4. Modelling 5. Simulation 6. Results and discussion 7. Conclusion
- 17. Real plant vs. Ideal plant Real plant: 115% more energy consumption 98% more carbon emissions All previous values based on real efficiency recalculated based on 100% efficiency for the components involved in each work station. 0 250 500 750 EnergyUsage(MWh) Real plant Ideal plant 0 50 100 150 200 250 TotalEmissions(Ton.CO2) Real plant Ideal plant
- 18. Optimisation Scenario Benefits after replacement of furnace: 18% less energy consumption 14% less carbon emissions 28% cost reduction in natural gas 0 250 500 750 EnergyUsage(MWh) Real plant New plant 0 50 100 150 200 250 TotalEmissions(Ton.CO2) Real plant New plant 0 2500 5000 7500 10000 NatralGasCost(£) Real plant New plant
- 19. Conclusions Modelling and simulation tools can contribute to: Identification of high energy consuming components Reduction of energy consumption and CO2 emissions Increase in money savings Enhanced decision making in environmental and production performance issues Potential to be embedded in new product development process. 1. Introduction 2. Case study 3. Manufacturing system 4. Modelling 5. Simulation 6. Results and discussion 7. Conclusion
- 20. Thank you for your attention
- 21. References 1. Kalla D., Twomey J., Overcash M., Methodology for systematic analysis and improvement of manufacturing unit process life cycle inventory, 2010, Wichita State University 2. Rajemi M. F., Energy Analysis in Turning and Milling, 2010, University of Manchester, School of Mechanical, Aerospace and Civil Engineering 3. Tran K., Study of Electrical Usage and Demand at the Container Terminal, PhD Thesis, 2012, Deakin University 4. M.E. Eltantawie, Design, Manufacture and Simulate a Hydraulic Bending Press, 2013, Int. Journal of Mechanical Engineering and Robotics Research 5. W. Trinks, M. H. Mawhinney, R. A. Shannon, R. J. Reedand J. R. Garvey, Industrial Furnaces, 2004, John Wiley & Sons, Inc 6. Rooda J. E., Vervoot J., Analysis of Manufacturing systems, 2005, Technische Universiteit Eindhoven, Department of Mechanical Engineering
Editor's Notes
- The objective of this study is the environmental impact assessment of a production system. Using appropriate indicators related to energy consumption and carbon emissions and discrete event simulation such as WITNESS, we can estimate the impacts from production processes on the environment.
- The objective of this study is the environmental impact assessment of a production system. Using appropriate indicators related to energy consumption and carbon emissions and discrete event simulation such as WITNESS, we can estimate the impacts from production processes on the environment.
- The case study is based on a manufacturing company which produces equipment such as rails and railroad parts. The under investigation production system consists of three departments: heat treatment, machine shop and finishing focusing on the production of 10 different variants of casting crossings, which have similar geometry but different total length and width.
- The first section is the heat treatment department. In the figure the process flow of the crossings through this department appears. The crossings are loaded by an overhead crane on a charger which first loads the crossings in a furnace for heating at 1060 C. After this the crossings are quenched in a water tank, again using the charger. Finally the charger comes back to the initial position where the crossings are unloaded by the crane to a storage area.
- The other two parts of the production are the machine shop and the finishing department. The crossings are loaded in each work station by overhead cranes and unloaded from the machine shop to the finishing department using forklifts. The machine shop consists of a hydraulic press machine, two manual milling machines and 3 CNC milling machines. The finishing department includes three working stations where operators using hand held grinders remove any imperfections from the crossings surfaces.
- The other two parts of the production are the machine shop and the finishing department. The crossings are loaded in each work station by overhead cranes and unloaded from the machine shop to the finishing department using forklifts. The machine shop consists of a hydraulic press machine, two manual milling machines and 3 CNC milling machines. The finishing department includes three working stations where operators using hand held grinders remove any imperfections from the crossings surfaces.
- To analyze and model the system at a detailed enough level, an input-output approach was used, taking into account parameters such as product demand, setup and process times.
- Three distinct areas are included in the Witness model, Heat treatment, machine shop and finishing. For each component and machine the energy consumption of the greatest consumers has been modeled, such as fans, pumps and motors.
- The simulation period assumed to be 240 working days, 3 shifts with 7.5 h per shift. The total annual production volume is 800 crossings following this production mix for each one of the ten different variants of crossings.
- The final results from the simulation have indicated a total energy consumption of 722500 kwh and 206000 kg of CO2 emissions. We can observe that 60% of the total energy is provided by natural gas and half of the emissions are caused by the consumption of electricity. However to understand if these results are good or bad relative to the overall environmental performance of the system, the current system was compared to the ideal equivalent system which would have 100% of efficiency.
- The results of the comparison indicated that the real system consumes 115 % more total energy than the ideal and emits 98% more CO2 during the production period. Thus it is obvious that there is a need for improvements from an energy consumption point of view.
- Another way to use this methodology is for assessing the performance of production systems when a component of the system (e.g. a milling machine or CNC) changes. In this case it was assumed that the furnace is replaced by another one 10 % more efficient than the old. The results show that the total energy consumption have been reduced by 18% while the total emissions have been reduced by 14 %. Moreover considering the average cost unit for natural gas, the purchase cost is calculated to be 28% less.
- As a conclusion it can be said that by applying the proposed methodology combined with the discrete event simulation combined, high energy consuming areas or components within a production system can be identified leading to reduction of the energy consumption and carbon emissions and increase of money saving due to proper changes throughout the manufacturing system. Moreover this methodology can be extended to include further parameters and conditions relative to environmental or production performance issues, enhancing the process of decision making on these issues.