Smart Manufacturing and Energy Systems

Transcription

1 Smart Manufacturing and Energy Systems Thomas F. Edgar, University of Texas Stratos Pistikopoulos, Texas A&M University FOCAPO/CPC 2017 CPC/FOCAPO 2017 January 9,

2 Outline Smart Manufacturing and PSE DOE Institute on SM Case Study 1-Hydrogen Production Case Study 2-Heat treatment Furnace Conclusions 2

3 What is Smart Manufacturing? The ability to take action, in real time, to OPTIMIZE assets in the context of business strategies and imperatives SMLC, Inc. All rights reserved. 3

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5 Internet of Things Deception Connect your smartphone to your digital scale Then you will lose weight You have to do something else? 5

6 DOE Project Objectives Develop a prototype open architecture Smart Manufacturing (SM) Platform to facilitate extensive application of real-time sensor-driven data analytics. Demonstrate SM Platform applicability, interoperability and operational security on two diverse commercial test beds at Praxair and General Dynamics. Employ new sensors, models and operating strategies to reduce waste heat. 6

7 Technical Approach- Two Test Beds Install image-based temperature measurements on Steam-methane reforming (SMR) unit so that real-time model-based decisions can reduce energy use and increase productivity in an SMR unit. Steam-Methane Reformer Furnace SMR Temperature Distribution GD Production Line Number of tubes Average Temperature Tube Temperature (K) Install measurements and software to reduce energy use and increase productivity in heat treatment and machining of artillery shell casings and commercial metal parts. Deploy real-time data analytics and modeling to optimize heating and forging together with CNC machine operation, where materials property targets are influenced by furnace/machine conditions. 7

8 SM Test Beds: Platform-enabled Modeling Impacts Energy use optimization/waste heat reduction Distributed sensing, model-based control of zone temperatures Optimized sensor and actuator locations High-fidelity models used to gain operational insight (e.g., CFD) Praxair steam methane reformer (SMR) image analysis via array of IR cameras distributed actuation (70+ burners) hybrid (data/physics) reduced-order model with data reconciliation optimization of burner flows optimal camera and automatic valve placement (reduce numberof MVs, CVs=$) GD heat treatment furnace (metal parts) 3-D modeling of radiation heat transfer dynamic part movement optimization of gas flow setpoints to decrease energy use (8% NG reduction) heat recuperationreducesconvective heat losses (~30% NG reduction) 8

9 Smart Manufacturing: Cloud-based Infrastructure Centralized repository of manufacturing intelligence tools 9

10 Smart Manufacturing: Real-Time Visualization *See Kumar, Baldea, Edgar poster 10

11 Smart Manufacturing: Scalable Computing Resources Workflow for high-performance computing-assisted furnace balancing High-fidelity CFD model assists the EC-SMR model in providing accurate predictions SM platform provides computing resources to reduce CFD model simulation time 11

12 Smart Manufacturing Platform: App Marketplace Representative schematic of SM platform marketplace Industrial users can choose the right model (and customize) on a pay-per-use basis Facilitates low-cost industrial solution deployment in a quick time 12

13 Energy Intensive Industries 13

14 Patterns of Productivity Gains Transformations Supply Chain Flow Paper Heating Material Conversion Productivity Steel Metals Measure Control Optimize Drying Cooling Quality Right the First Time Variability Reduction Seams Context Optimize Glass Food Microelectronics Industrial gas Mixing Demand Management Coatings Plastics REAL-TIME Composites Aerospace Test Beds 14

15 SMART Manufacturing: Advanced Controls, Sensors, Models & Platforms for Energy Applications 15

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17 Northwest Institute Structure A Cyber Physical Network of Regional Smart Manufacturing Centers (RSMC) actionably linked through the open SM Platform and Marketplace Northwest Northeast California HQ Southeast --Regional concentrations of manufacturers --Established ecosystems training, universities, institutes, consulting, services, economic development Gulf Coast 17

18 What are the layers of Smart Manufacturing technology? 18

19 Power Grid and Hydrogen Production Smart Manufacturing Power Source Power supply Power Management Nuclear Base Line Power Power Coal and Liquids Renewables Natural Gas Renewable Conventional Mix Hydrogen Hydrogen Production mix

20 Hydrogen Production mix Smart Manufacturing Natural Gas Gas Turbine Electrolysis SMR/WGS CCS Wind Hydro H2 Storage Solar Fuel Cell Power Supply

21 Tools Smart Manufacturing Model Development System Intelligence Big data analysis Optimization and Advanced Control Framework implementation

22 PAROC - Framework Smart Manufacturing High Fidelity model Model analysis Experimental validation Model Approximation Multi-Parametric Programming Multi-Parametric Receding Horizon Policies Closed-Loop Validation Model based mp-mpc Controller

23 PAROC - Implementation Smart Manufacturing

24 Hydrogen System Components Smart Manufacturing Hydrogen Production Steam Methane Reform Water Electrolysis (sustainable) Hydrogen Storage Stationary Mobile Metal Hydride Hydrogen Usage Power Generation - Fuel Cell Industry

25 Model Development Smart Manufacturing High Fidelity Dynamic Model Development System model First Principle Sensors data analysis Model validation experimentally and in silico System model Component Model Unit Peripheral Model Flow Thermal Management

26 System Intelligence Smart Manufacturing Experiment Design

27 Sensor Data, Modelling and Validation Smart Manufacturing Sensor Data Analysis Sensor data collection Temperature profile data for the Proton exchange Membrane reaction Data analysis System Monitoring

28 Optimization and Advanced Control Smart Manufacturing MODEL/OPTIMIZER Control Actions Data - Measurements SYSTEM

29 The Iron and Steel Industry Overview Temperature, esp. the metal core, cannot be sensed and controlled directly In practice, operators tend to overheat Insufficient heating in only some portions adversely affects the product quality - waste Modeling and optimization directly impact overall energy consumption & related CO 2 emissions 29

30 System Description Heat treating (austenitization) Operated in a continuous manner under T feedback control Parts loaded on to trays placed on a conveyor belt through the furnace Natural gas fired radiant tubes in ceiling and floor 30

31 System Description After exiting the furnace, parts are quenched to induce desired mechanical properties Hardness, toughness, shear strength, etc. Nitrogen (inert gas) flows counter current to the direction of movement of conveyor belt Image Source:General Dynamics Scranton Operation Energy Demonstration Project 31

32 Model Structure Surfaces N 2 Exit Part Entry N 2 Entry Part Exit Surfaces 1-64 Burner load Insulation 130 furnace surfaces and 32 load surfaces 32

33 Numerical Solution Algorithm (PDEs) Initialize System Temperatures and valve openings Solve for Radiosities 1. Radiation model (instantaneous) i. Solve for radiosities (J values) given surface temperatures and burner heat duties Solve Radiation Model Resolve for Temperatures ii. Use radiosities to solve for temperature and design/insulator surface heat duties Check for Convergence iii. Repeat until system converges to desired tolerance Heat Boundary Conditions Ramamurthy et al. (1995) JMPEPG 4:

34 Numerical Solution Algorithm (continued) Heat Boundary Conditions Solve Load Temperature Profile (All Trays) Crank Nicolson Method Surface Temperatures 2. Load PDE Temperature Profile (Time Advances) 3. Move tray to the next location, iterate Advance trays, iterate Implemented in MATLAB View factor precalculated for speed (1 h simulation time for 25 hours of operation) Less than 10 min simulation time for 25 hours operation 34

35 Regulatory Control Layer T sp Sensor Locations, T + Feedback Controller Mass flow of fuel Control Zone 1 Control Zone 2 Control Zone 3 Control Zone 4 Manipulate flow in control zones individually to control zone temperature 35

36 Heuristic Set Points Temperature Profiles Temperature distribution of part no. 20 Heat transferred to part no

37 Real-Time Optimization + - Control System (Fuel Valves) Process Data Reconciliation Reconciled Data Optimized Zone Temperature Set Points Steady-State Optimization GOAL: Picking the Optimal Set Points of Zone temperature controller: - Reach desired minimum part temperature - An upper bound for the austenite grain size to ensure product toughness Seborg, D. et al. (2011) Process dynamics and control, John Wiley & Sons 37

38 Optimization Formulation min EEEEEEEEEEEE UUUUUUUUUU TT ssss,ii s.t. 900K T T σ T d sp, i + T part, exit part, exit part, exit part, exit T diff sp, i 1100K Process model 1300K T sp, i µ m Min/Max zone temperatures Increasing zone temperatures Min part temperature Temperature spread Grain growth restriction 38

39 Response Surface (RS) Modeling LHS for initial test data Use LASSO to fit basis functions Solve optimization using surrogate model Use optimal solution as input to model Good Solution? Process model is procedural: not suited for optimization Latin Hypercube Sampling (LHS) can be performed efficiently (100 data points) RS basis functions: functional form accounts for fourth-order dependency of temperature in radiation heat transfer z = β T β T β T T3 + β T β T β9t + β10t + β + β12t + β T T + β T T + β T T + β T T 1 + β T β T T 2 + β T β T T Wachter, A. and Biegler, L. (2006), Mathematical Programming, 106(1), pp β T Optimization problem (prev. slide) solved by interior-point optimization solver IPOPT in Matlab 39

40 Optimized Set Points Grain Size Evolution Grain size distribution of part no. 20 High temperature regions of the parts have larger grain sizes. Maximum grain size larger than 40µm because of inaccuracies in the surrogate model. 40

41 Optimized Vs. Heuristic Heat sources and sinks Heuristic set points Optimized set points Total energy input per part (GJ) Part minimum temperature (K) Coefficient of variation Maximum grain size (µm) Energy to parts (%) Heat lost to Exhaust (%) Heat lost to blanket nitrogen (%) Heat lost through insulation (%) Energy Savings 8.98 % Obtained from operating at constraint Significant heat losses require system reconfiguration: use furnace exhaust to heat inlet air/fuel? 41

42 Conclusions The Smart Manufacturing paradigm encompasses process modeling, data analytics, control, optimization, and manufacturing Successful demonstration of SM on test beds will aid in dissemination and evangelizing Saving energy is an important metric that improves profitability while addressing sustainability Development of a vendor-agnostic SM platform and app marketplace will accelerate software integration and adoption of SM in a wide range of industries 42