Energy-Aware Manufacturing. Vittal Prabhu, Professor Marcus Department of Industrial and Manufacturing Engineering Penn State University

Transcription

1 Energy-Aware Manufacturing Vittal Prabhu, Professor Marcus Department of Industrial and Manufacturing Engineering Penn State University

2 Motivation for sustainability We, as a species, are depleting many resources at a very rapid rate Fresh water efficiency ~ 40% Car efficiency ~ 25% Light bulb (incandescent) efficiency ~ 2% Engineers and managers should take a holistic perspective of products/services from design, manufacturing, operation, transportation, and recycling

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4 VISION: ENERGY-AWARE MANUFACTURING Energy Markets Price Dynamics Product Demand Market Dynamics Environment Weather Dynamics Green Fleets for JIT Delivery Factory Power Var. Model Control for SmartGrid Green Factory Physics Energy-Aware Logistics Energy-Aware Production Energy-Aware Processing Energy-Aware Design Decisions/Information Decisions/Information Decisions/Information Nimble Green Fleets Supply Chain Optimization Smart Grid Cyber/Physical System Factory Design Production Control Pervasive Sensors Process Modeling Real-time Control Energystar-like Benchmark Energy Foot-print Data Analytics Acknowledgements: Graphics adapted from U.S. DoE, Wikipedia 4

5 Green Factory Physics

6 Motivation for energy efficiency in manufacturing Need to understand and manage the energy consumed in discrete manufacturing systems Quite frequently power consumed during idle time is a significant (40 ~ 80% of production) Energy consumption of individual equipment influenced by the higher-level production control policies Thereby influence the overall energy consumption, energy efficiency and production performance

7 Modeling energy consumption in milling VF3 Machine Steel Block (6 x 4 inches) Experimental measurements Steel work piece of 6 x 4 inches and a 4-inch cutter on Haas VF3 Measured using Fluke Power Logger 1735 with a sampling period of 0.5 seconds

8 Power Consumption (kva) Pass No.1 Pass No.2 Pass No.3 Pass No.4 Pass No.5 Pass No.6

9 Machine States and Power Five machine states, and power consumption levels o Setup: Generic machine state of waking-up with WW SS o o o o Tool Change: State for tool (cutter) changing with WW TT Cutting: State of air or material cutting with WW CC Nominal Power Idling: Idle state whose duration is less than ττ with WW NN Low Power Idling: Idle state whose duration is greater than ττ with WW LL Power measurement with a Haas VF3 milling machine 8 Power (kva) Time (seconds)

10 Idle Production States Busy T s P max P low Ramp Up Time Off Standby Wait Process T P cut cut P air T air Energy States Ramp Down Time

11 HySPEED Screenshot

12 Energy dynamics in a 4-machine serial production system

13 Opportunity to model interplay between production/machine level Manufacturing System Schedule Batching Setups Machine Process Material Factory Level: Production Control Policies Interplay Equipment Level: Energy Control Policies Cycle Time Throughput Inventory Utilization Availability Energy Decisions Model KPI

14 Machine-level Distributed Intelligence Parts influence production timing & energy cost Machines influence production capacity & energy waste d i (t) e(t) + Manufacturing Performance Controller a i (t) + c i (t) Energy Cost Controller + q i (t)+p i (t) Idle Power Controller Machine Capacity Controller

15 Policies Modeled Production control PUSH, simple, widely understood Energy control policy - EC1: if idle time > τ i then switch machine to lower power state If production schedules are firm for at least τ, then EC1 can be implemented easily Low hanging fruit for improving energy efficiency!

16 Basic M/M/1 queuing model λ 1 µ 1 Wasted Energy Arrivals Queuing Low power ProcessingDepartures idling Nominal idling power EE ww = WW 0(1 ρρ)ee λλλλ EE pp WW pp ρρ Productive Energy + WW 1(1 ρρ)(1 ee λλλλ ) WW pp ρρ Processing power

17 Impact of utilization and idle time threshold (10-machine line)

18 Effect of lowering idling power and idle EE EEEEE llllllll EE EEEEE llllllll = mm ii=1 time threshold WW 0ii 1 ρρ ii ee λλττ ii + WW 1ii 1 ρρ ii (1 ee λλττ ii) mm ii=1 WW 1ii 1 ρρ ii

19 Comparison of analytical model and simulation experiments (4 machines) EC1 ON EC1 OFF Analytical Exponential Normal Hyper-exp Hypo-exp

20 Simulation experiment (CMOS fab) (5) Start (1) ~PPPPPPPPPPPPPP rrrrrrrr λλ Lithography (L) (4) Deposition (D) (2) (3) End (6) Etching (E)

21 Case Study with Gi/M/1 Simulation Interarrival time fit using Generalized Erlang Distribution for estimating the energy demand of all three manufacturing steps.

22 Histogram and Fitted GED

23 SME Case: 400 ton Progressive Die Press Power (W) T s1 T s2 T s3 T p 0 13:34:30 13:35:01 13:35:32 13:36:03 13:36:34 13:37:05 13:37:36 13:38:07 13:38:38 13:39:09 13:39:40 13:40:11 13:40:42 13:41:13 13:41:44 13:42:15 13:42:46 13:43:17 13:43:48 13:44:19 13:44:50 13:45:21 13:45:52 13:46:23 13:46:54 13:47:25 13:47:56 13:48:27 13:48:58 13:49:29 13:50:00 13:50:31 13:51:02 13:51:33 13:52:04 13:52:35 Power consumption has been calculated by hooking a power meter to the machine Set up time: T s1, T s2, T s3 Production time: T p Δ (theor/pract) = 30% Acknowledgement: R. Gandhi, B. Kishore, A. Narasimharaju, P. Pecorario

24 SME Case: 400 ton Progressive Die Press Different Scenarios have been taken into account for the energy consumtpion during the manufacturing process Number of Coils Produced a day 1/2 Coil 2/3 Coil 3/4 Coil 1 Coil 1.5 Coils 2 Coils 2.25 Coils 2.7 Coils Working hours a year Number of coils produced a year Number of coils produced a day ρ=λ/μ Pairs produced per year Coils produced per year Number of Coils Produced a day 1/2 Coil 2/3 Coil 3/4 Coil 1 Coil 1.5 Coils 2 Coils 2.5 Coils 3 Coils E0 E1 E best case Acknowledgement: R. Gandhi, B. Kishore, A. Narasimharaju, P. Pecorario

25 Key Outcomes Easy to use Excel based detailed simulation tool Leverage existing analytical models to include energy control for waste reduction energy aware queuing model Proposed analytical models can be readily used to estimate reduction in energy waste for different production and power parameters within about 10% of detailed simulation models

26 Control for SmartGrid

27 Control for the SmartGrid Demand Supply Source: NETL, DOE, Lawrence Livermore National Lab

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29 Smart Manufacturing + Smart Grid 10 Varying price with time of use c/kwh Time Power from Grid Price varies with time Power from Renewable Sources Power varies with time Production demand Smart Manufacturing Production schedule Consumption schedule Storage schedule Storage

30 Production Production controller Due-date Manufacturing performance controller Arrival Time Due-Date Deviation Energy cost controller Target energy cost Energy cost controller Energy cost MaintenanceDeviation Request Time Process controller Nominal capacity Capacity controller Expected energy consumption cost Resource Capacity Machine Capacity Variations Machine

31 DISTRIBUTED CONTROL d i (t) e(t) Parts influence production timing & energy cost + Manufacturing Performance Controller - + a + + i (t) c i (t) Energy Cost Controller + Machines influence production capacity & energy waste q i (t)+p i (t) Idle Power Controller Machine Capacity Controller tt aa ii (tt) = kk ii zz ii (ττ)dddd 0 kk ii dddd dddd + aa ii(0) 31

32 INTUITION BEHIND THE CONTROLLER If only ECC is used then controller will try to energy cost If only MPC is used then controller will try to improve manufacturing performance (JIT) When both ECC and MPC are used, controller will try to balance manufacturing performance and energy cost What are the resulting dynamics? 32

33 Example of dynamically varying price of power Price e(t) c Time

34 Combined dynamics of MPC + PPC 10 8 Processing Sequence <1,2> a (t) M 10 8 Processing Sequence <1,2> a (t) M 6 Kz<2,1> Convex hull 6 Kz<2,1> Convex hull 4 a(t) Kz<1,2> 4 a(t) Kz<1,2> 2 Processing Sequence <2,1> 2 Processing Sequence <2,1> With only MPC With MPC + PPC

35 Production dynamic by power cost variations (3-part)

36 Example: 2 parts parts, k= Arrival Time #2 [s] kp=0 kp=0.05 kp= kp=0.5 kp=1 120 kp= Arrival Time #1 [s]

37 Example: 50 parts MSD x MPC

38 Factory Emission Modeling

39 Distributed Controllers for Managing Emissions in a Manufacturing Supply Chain <ATO manufacturing and control scope>

40 Distributed Controllers for Managing Emissions in a Manufacturing Supply Chain <Inventory variation> <Processing time variation> Due date deviation cost Total cost Emissions cost <Cost variation >

41 Green Fleets for JIT Delivery

42 Greening via energy & emissions in transportation A feedback control algorithm for Greening via Energy and Emissions in Transportation is developed for capacitated open vehicle routing problem with time windows (COVRPTW) Analyzing the closed-loop dynamics of the algorithm by using techniques from discontinuous differential equation theory. Analyzing just-in-time delivery performance and GHG emissions <Control theoretic approach for departure time and vehicle cruising control>

43 Greening via energy & emissions in transportation (GEET) Routes with static vehicle cruising Routes with dynamic vehicle cruising Comparison MSD with fuel consumption

44 Thank you! Questions?

45 Analysis of thermal loads and energy calculation of Leonhard Fame lab(level1) Annual Building Utility Performance Summary Values gathered over 8760 hours