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Smart Manufacturing: Encouraging Paradigm Shifts In Process Operations-
-
Energy to GHG, Water Performance, Sustainability
Indicators
Darlene Schuster Director Technical Entities, AIChE
November 2015
AIChE is on the frontier of chemical engineering research and development
to help address critical issues of today and tomorrow, as the incubator and
ongoing supporter of national high-tech knowledge centers.
Professional Development
& Technical Societies
www.aiche.org
Technical Entities (TEs): Providing Solutions for Society’s Grand Challenges
Center for Energy Initiatives
International Society for Water Solutions
Institute for Sustainability
Society for Biological Engineering
- International Society for Metabolic Engineering
Industrial Technology Alliances (ITAs): Providing Solutions for Industry’s Challenges
CCPS & DIERS address process safety
CHO II addresses pharmaceutical development & quality
CSTP & Cosmetics roundtable cover sustainability-- and metrics
DIPPR addresses quality of physical properties measurement
CCPS/DIERS CHO II CSTP DIPPR
National Network for Manufacturing Innovation (NNMI)
ON SMART MANUFACTURING
SMART IS here to stay…. DOE has announced a RFP for a
NNMI on SMART
Federal Plan to help revitalize American manufacturing and encourage companies to invest in the United States. (2012)
United States Grand Challenge: Companies, innovation, jobs, leaving the US
Current and potential NNMIs Name
Executive Director
Sponsors*
Year Formed Products & Services
America Makes Ralph Resnick 100 2012 3D printing, also known as additive manufacturing
Lightweight Innovations for Tomorrow
Lawrence Brown
6 2014 Lightweight technology development
Institute of Advanced Composites Manufacturing Innovation
Doug Parks – Chair
19 2015
Reduces technical risk and develops a robust supply chain for advanced composite materials in applications such as auto, wind turbines and compressed gas storage
Power America David
Danielson 3 2010
Develops advanced manufacturing processes that will enable large-scale production of wide band gap (WBG) semiconductors
Digital Manuf’g & Design Innovation Institute
Dean Bartles 12 2014 Encourages deployment of digital manufacturing and design technologies to become more efficient & cost-competitive
SMART ? ** SMLC –D. Swink * 20 2016** SMART Process Operations
Process Intensification?
?? ?? 2017? Process Design and Construction for industry
** In development and anticipated awarded in 2016
Sponsors include companies, non-profits, universities, governmental agencies
Defn of SMART
Smart Manufacturing (SM) is the application of real-time, networked and data based manufacturing Intelligence to facilitate dynamic market demand, added product value, and high velocity technologies and products with increased expectations for environmental sustainability and zero safety incidents.
Smart Grid
Customer
Enterprise Business
System
Suppliers
OEM Machine Builders
Distribution Center
Factory
• Higher value products
• Improved quality
• Zero downtime
• Increased equipment
life / utilization
• Improved safety
• Reduced energy
and emissions
• Highly sustainable
• Higher product availability
• No inventory
• Product lifecycle
management
8
ENTERPRISE OPTIMIZATION &
SUSTAINABLE PRODUCTION
ENERGY,
SUSTAINABILITY, EH&S AGILE DEMAND-DRIVEN
SUPPLY CHAINS
Courtesy of Rockwell Automation : Copyright © 2010 Rockwell Automation, Inc. All rights reserved.
The commitment to comprehensive design-manufacturing life cycle
T H E
V I S I O N
Open Architecture
Infrastructure &
Apps Store
Smart Manufacturing as a Real-Time Networked Information Enterprise
Open-architecture infrastructure for plug & play
Workflow Based architecture for composability and insertion
Composable sensor-based SM Systems
“Information that drives the next century’s structural shift in manufacturing.”
SMLC Partnerships
An Industry-Driven
Open Architecture
Shared Infrastructure
Smart Manufacturing
Smart Manufacturing Leadership Coalition (SMLC) – 501c (6)
Making real-time info available: • when it is needed, • where it is needed • and in the form it is needed throughout the Manufacturing ecosystem
Test Beds - General Dynamics, General Mills,
General Motors, Praxair, Corning, Pfizer, NETL,
Alcoa, Center for Advanced Technology Systems/RPI
Design/Manufacturing Platform Providers –
JPL/NASA, UCLA, Rockwell, Honeywell, Emerson,
Invensys, Nimbis Modeling & Simulation Materials,
Design, Manufacturing – Caltech/JPL, NETL,
Argonne, UCLA, UT Austin, Tulane, NCSU, CMU,
Penn, Purdue Smart Manufacturing/Smart Grid –
EPRI Global Metrics/Outreach – AIChE, ASQ, AMT,
ACEEE, NCMS, MESA, MT Connect, Sustainable
Solutions, Spitzer & Boyes Agency partners – NIST,
NSF Regional Partners - Center for Smart
Manufacturing Innovation in CA (CMSI), Wisconsin
Manufacturing Institute Other - Association of State
Energy Research & Technology Transfer Institutions
(ASERTTI), National Association of State Energy
Officials (NASEO)
Design Operations Supply Chain
DOE Project Test Bed 1
Praxair dynamic energy management & cross unit performance
DOE Steam Methane Reformer (SMR) Project Overview
• Demonstrate Smart Manufacturing platform for energy saving applications
• Praxair Goals – Evaluate next generation technology for improved reformer temperature management
– Reduce energy costs and emissions while improving reliability
• Project started in September 2013. Port Arthur facility is the beta site – Partners: UT (modeling); Emerson (control); UCLA/Nimbis (IT platform); AIChE (metrics)
12 | Praxair Business Confidential | 12/15/2015
Technology for improved efficiency and reliability
-1.5
-1
-0.5
0
1400
1450
1500
1550
1600
0 5 10 15 20 25 30 35min
SMR Temperature (F)SMR Pressure (inWC)
Tube & Pig Tail TCs, and fixed IR imaging
Data driven and fundamental models Cloud based applications
ROT performance monitoring Manual burner valve adjustments
Measurement Modeling Monitoring and Control
13 | Praxair Business Confidential | 12/15/2015
Instrumentation Installation Completed at Port Arthur
North Side Wall
South Side Wall
4 cameras
Instrumentation ‒ 8 fixed infrared cameras ‒ 70 thermocouples ‒ 8 furnace fuel flow-meters ‒ Associated wireless/wired communication
~$1.1 MM
Sensing in ~80% of tubes
Row A Tubes
Tunnel A
Tunnel H
Row G Tubes
West Wall
4 cameras
Energy Intensity Reduction
Continuous Improvement
DOE Testbed #2
SMLC Project
Line Operations
DOE Project Test Bed 2 Energy Intensity Next Step improvement
General Dynamics Multi-Function Energy & Integrated Line Management
Heating and Forging Cutting and Machining
Power Mgmt and Energy Grid
16
Supply Chain Distribution Center
Customer
Business Systems, ERP
Smart Grid
Smart Factory
Recipe Management
Mapping formula into operating
recipes
EDI transaction
& quality
certifications
FDA Tracking &
traceability
Example Other Test Bed - General Mills
Green Light
Analyze - to put into production
Make – right ingredients – confirmation on recipe
Release – meet requirements to release
Mapping SAP information
Into operation
Workflow Construct Machines – People - Materials Dynamic Manufacturing Ecosystem
Design Data
Product Manufacturing
Materials & Process Tech
Prototype Qualification In
Service
Macro Layer
Meso Layer
Micro Layer 1000s
control loops
Time - minutes
100s
control loops
Time -hours
10s
control loops
Time – days
Focus: 10x Multiple
Pass Variability
Reduction; Supply
Chain Information
Bu
sin
ess
Sys
tem
s
Co
ntr
ol &
Au
tom
atio
n
Focus: 100x Event
Variability/Tradeoff
Adjustment; Dynamic
Performance Mgmt.;
Integrated Metrics
Focus: Insertion,
Qualification, ICME, High
Fidelity Dynamic
Operations
Review of Past and Existing Metrics
• Sustainability/sustainable development metrics/indices abound – EPA Framework for Sustainability Indicators: Database for Sustainability
Indicators and Indices
– NIST’s Sustainable Manufacturing Indicators Repository
• Start with AICHE Sustainability Metrics, monitor, finalize
Goals
KPIs
Metrics
Data
Material Intensity Mass of Materials Consumed - Mass of Products
Output
Basic Sustainability Metrics
Toxic Release Mass of Recognized Toxics
Output
Solid Waste
Output
Mass of Solid Wastes
Output:
Mass of
product
Functional
unit
Revenue
or
Value-
Added
Value-
Added
Energy Intensity Net Energy Consumed in Primary Fuel Equivalents
Output
GHG Emissions
Output
Pounds of CO2e
Pollutant Emissions Pounds of Pollutant Equivalents
Output
Water Consumption Volume of Fresh Water Consumed
Output
Developed by AIChE Industry roundtable cwrt (IfS) 2001
Enterprise Engagement with decision suport—KPI’s they need
• Responsibility for improving energy productivity and sustainability optimization belongs to all functions, e.g.
– Sales & Marketing – develop energy efficient portfolio of products – Finance – utilize accurate cost model for allocating energy use to products – R&D – develop energy efficient products both in manufacturing processes and customer
use – Process Engineering – identify annual energy cost savings in design stage of capital
projects – Production Engineering –assure yield and quality (and energy costs?) – Operations –reliability and asset optimization
Sales and Marketing Engineering Logisitcs Product Use Customer Use Disposal
Procurement Operations
Raw Materials DistributionManufacture Industrial Customer Use Disposal/ recycleEnd Use
Process Map for Business to Business
Process Map for Business
Focus Groups—who uses water data in making key decisions?
Responsibility for Water in Industrial complex?
How is data used in decision making?
Key data needed for Metric for KPI for decision maker.
NEXT STEPS?
Praxair Testbed
Auxiliary Utilities Used in process, not in
product
Reformer/Purification
Raw Materials
Recovered heat/ Steam
Water Input
Off-gas, Fuel
By-products used in other processes
On Site Waste Treatment
Purchased Off-site Energy
Energy Generated On-Site
Fuel
Product
Off Site
Emissions
Steam and/or electricity
Emissions
Fuel
Waste Haz and non-haz
Reformer Boundary
Water Discharge
Emissions
Facility Scale
Auxiliary Utilities Used in process, not in
product
Manufacturing
Raw Materials
Recovered heat/ Steam
Water Input
By-products used in other processes
On Site Waste Treatment
Purchased Off-site Energy
Energy Generated On-Site
Fuel
Product
Off Site
Emissions
Steam and/or electricity
Emissions
Fuel
Waste Haz and non-haz
Facility boundary
Emissions
Water Discharge
Non-quantitative Metrics at the Macro level
1. Contribution to National energy security 2. Compatibility with requirement for regional collection,
preprocessing and shipping. 3. Potential for co-use of land and/or production facilities by
other foods and/or feedstocks that have a different production cycle
4. Impact of changing practices that change habitats (e.g. biodiveristy, non-native species, etc.)
5. Compatibility with existing farming practices including financing and risk management.
6. Potential to develop supply “leveling” storage
Workflow, Data,Time & Cost
Smart Manufacturing Open Architecture
Platform
Control & Automation Propriety Optimized
Automation Workflows
Data Partnership Workflow
Orchestration
Toolkits
Sensor Data
Apps Store
Data collection, modeling & Synchronization multi-scale time requirements embedded
Data collection, modeling & synchronization defined by workflow
Single scale time requirement in workflow
Decision
Composability Design to manufacturing Workflow libraries
Separate Data & apps Data to apps paradigm
Generalized Energy Performance and Productivity Metrics Development
• Metrics Experts
• SMLC Collaborators
AIChE, SMLC, NCMS Coordination Team
incorporation of sustainability metrics for energy---Productivity Suite of Metrics
Implement metrics suite and refine as needed for
productivity improvement
Application of generalized
metrics concepts to
broad base of industries
Develop generalized “core” for variety of platforms
Base data from Test Beds and key data
questions
Input from additional stake holder industrial
platforms
‘The Coming Tech Led Boom’ Wall Street Journal, Jan 30, Mark Mills & Julio Ottino
“The Internet is evolving into the "cloud” — a network of thousands of data centers, any one of which
makes a 1990 supercomputer look antediluvian….Astronomical feats of data crunching enable
heretofore unimaginable services and businesses - we are on the cusp of unimaginable new markets.”
Smart manufacturing. This is the first structural shift since Henry Ford launched the economic power
of "mass production. Engineers will soon design and build from the molecular level, optimizing
features and even creating new materials, radically improving quality and reducing waste.”
“The implications of the radical collapse in the cost of wireless connectivity are as big as those
following the dawn of telegraphy/telephony. Coupled with the cloud, the wireless world provides cheap
connectivity, information and processing power to nearly everyone, everywhere – introduces rapid change and great opportunity.”
Cloud Computing & Collaborative Manufacturing
Feedstock Selection
•Petroleum-based
•Natural gas-based
•Bio-based
Next Gen Unit Operations
•Process intensification
•Design optimization
•Skid or modular units
• Innovative separation
Product
•Assure yield
•Minimize waste
•Assure quality and consistency
Traditional CPI
Pharma and Biologics
Automotive
Energy Industries
Food
Modeling Simulations
Practical minimum energy
Sustainability productivity metrics
- Materials
- Water
- Toxics
- Energy
- Land use
Micro, Meso & Macro Levels
Supply Chain
Data Cloud
Security
Project SM
Lifecycle
A Second NNMI under consideration
• Process Intensification
• Systems and Process Intensification Network
• http://processintensification.org/2014
What is Process Intensification (PI)?
*National Science Foundation Workshop on Process Intensification, October 2014, www.processintensification.org, Adapted from European Roadmap of Process Intensification. 2007
Process intensification is a set of often radically innovative principles (“paradigm shift”) in process science, chemistry and equipment design, which can bring significant (more than factor 2) benefits in terms of process and chain efficiency, capital and operating expenses, quality, wastes, process safety, etc*.
Process Intensification is not routine,
incremental process improvement!
Manufacturing and ChemE
Idea
generation
Lead
Definition
Pre –development
Develop-ment
Commercial-ization
Research Push NOT Industrial Pull
DOE has indicated they will announce a Request for Proposals (RFP) for an NNMI on SMART
Another possible NNMI on Process Intensification may
be announced in 2016
• Energy savings 20-80%
• Capital and life cycle savings 20-80%
• Selectivity and yield increase up to >10 times
• Significant process safety increase - reactor volume & inventory of chemicals decreased
10-1000 times + better reaction control
- 25% reduction in safety events
• Efficiency 40% reduction in cycle times
• Execution 10x improvement in time to market in target industries
PI/SM: Reducing Project Risk
34 34
Realized Benefits: PI and SM
• Reduce capital and life cycle cost
• Reduce hazardous material at risk
• Reduce process sampling and characterization points
• Reduce plant footprint
• Enhance worker safety
Chemical Manufacturing Improvements
• Originally adopted to provide intrinsic process safety
• Developed into a tool for project risk reduction
Current Processes
• Less efficient: labor-intensive
• Lower output and productivity
• Lower quality products
• Lower paying unskilled jobs
• Higher risk working conditions
• Higher environmental impact
• Higher production costs
• Rigid, high-volume production
• Slower time-to-market
Intensified Processes
• More efficient: automation-intensive
• Higher output and productivity
• Higher quality products
• Higher paying skilled jobs
• Safer working environment
• Less waste, less resource use
• Lower production costs
• More flexible customization
• Faster time-to-market
Current vs Intensified Processes
PI is often accomplished through chemistry changes with no change in the equipment-For example, the separation efficiency in liquid-liquid extraction can be dramatically increased by changing the properties of the solvent
Elements of Process Intensification
Architecture Process
And Engineering Model
External Network
Secure Network
Machining Furnaces
Microsoft
SQL Server®
File Share
Boundary
Solution Engine® • Aggregation • Analytics • Rules
Solution Engine® • Cloud Connection
REST – HTTPS
Interface
Plant Displays
IR Cameras
39 | Praxair Business Confidential | 12/15/2015
40 | Praxair Business Confidential | 12/15/2015
Reformer Balancing - Motiva Port Arthur
Thermocouples and Flow Meters
41 | Praxair Business Confidential | 12/15/2015
Thermocouples will help validate camera measurements
TC Extractopad TC V-Pad
Flow meters Transmitters
Gas Fired Furnace
• Pre-existing thermocouples and pressure sensors will be utilized to Monitor furnace output in several segregated zones. • Furnace is scheduled back into production January 2016
Hot Forging
• Water Flow Meters and Sensors have been designed in order to detail operational temperatures with in the inside diameter of the • Forge tooling. In addition pre-existing billet temperature thermo camera data will integrated for product heat variation and consistency • M795 Projectile Production has been scheduled for January 2016.
Heat Treatment
• Thermocouple Sensors and Furnace Pressure Sensors have been installed. • Quench Oil Temperature sensors were returned after evaluation and new more
durable sensors have been ordered • Furnace Recuperators (Inkind investment) have been successfully trialed • Production Run for Heat Treat Furnace scheduled for 11/9/15
Integration Status Project Area Status
Furnace Instrumentation In Process
Machine Tool Instrumentation Awaiting MTConnect® Adapter Installation
Plant Server Staged at RTD Offices • Solution Family Installation Complete • Testing Complete OPC/MTConnect® • Await OSI Server Credentials Waiting model integration before plant delivery
Model Development Data Definition • Completing First Iteration of Model Definition • Building Simulation of Data Production on Plant
Server Gathering Complete Tag Point List from Furnaces
Model Testing Await Completion of Definition and Installation
S M L C
O B J E C T I V E S
Develop a standards-based reference architecture based on
industry-driven collaboration with IT suppliers
Create and provide broad access to next-generation sensors, including low-cost sensing and sensor fusion
technologies
Lower cost barriers for applying advanced data analysis, modeling, and
simulation in core manufacturing
processes
Build pre-competitive infrastructure including
network and information technology,
interoperability, and shared business data
Ensure multi-level cyber security and
protection at a scalable level
Integrate requirements of
small, medium and large enterprises
Facilitate efforts to secure funding through public-
private and private-private partnerships to address priorities
Establish an industry-shared SM Platform that includes an open
architecture software development framework
Implement R&D projects for joint investment and
execution of SM Systems
Operate industry test beds for Smart Manufacturing System
concepts and make them available to companies of all sizes
What is Smart
• Orchestration of standardized decision workflows based on structured adaptation and autonomy
• Applications that can share data, data that can share applications and applications that can connect to applications to achieve horizontal enterprise views and actions
• Actionable data, trust and visibility across supply chain
• In time, in production qualification of materials, products and actions
• In time, in production multi-dimensional (business, operations, supply chain, customer, maintenance, energy) performance and adaptation
• Cross-company operational data to improve
performance
• Evolvable design models in manufacturing
Orchestrated
Decision-making, resilience, autonomy and/or adaptation
With enterprise (end-to-end) contextual data and understanding
While being interoperable, accessible, affordable, secure and reliable
47
Workflow (WfaaS)
WfaaS Integrated with SaaS, PaaS, IaaS
Data-based Workflow Orchestration
Proprietary Workflow
Provisioning
State Tasking
Orchestration
Security Provenance
Application Instances
Application Images (EC2 AMI, OS Glance)
Kepler WFs ISV Software
ComputeCluster
Scratch Scratch Scratch Scratch Scratch Scratch Scratch Scratch
Infiniband
Cloud Compute Layer (EC2, OS Nova, Azure
Compute)
Compute Hardware and Networking
Cloud Object Storage (AWS S3, OS Swift, Azure Storage)
Audit Logs
Actor Data
Access Contr
ol
Actors Provenance Data
Management
Composing Workflows
Data Rights Security
SMLC SM Open Workflow Development & Deployment
User FS
Instance
Workflow Manager Instance
User FS
Instance
Cluster
Instance
Workflow Actor Instance
Streams Manipulators Dashboard
SMLC Hardened Marketplace Buyer WF
Dashboard Buyer
Catalog Portal Apps
Ecommerce Service
Broker
SM Workflow Perspective
Application Images
(Glance)
Object Storage (Swift, Ceph)
OpenStack Cloud Deployment Identity
(Keystone) Virtual Private
Networks (Quantum)
ComputeCluster
Scratch Scratch Scratch Scratch Scratch Scratch Scratch Scratch
Infiniband
Compute Resources (Nova)
Compute Provider Interface (CPI)
ComputeCluster
Scratch Scratch Scratch Scratch Scratch Scratch Scratch Scratch
Infiniband
Shared Storage
Remote Access License
Manager Application Images
IaaS UCLA
Nimbis/Cisco
Rackspace
Amazon,
Azure,
HP
Manipulators Streams Actors Provenance I/O Date Mgm’t Composing Workflows
Data Rights Security
SMLC SM Open Workflow Development & Deployment Dash Board
Marketplace
Validated
Workflows
Company
Workflows
& APPs
R&D
Workflows &
APPs
Marketplace
Commercial
APPs
Open Source
APPs
DOE SM Testbeds (Praxair and GD)
Future SMLC Testbeds
DOE SM Nimbis & UCLA
OpenStack Hosted Platform
SMLC/Nimbis Service Platform
( SM PaaS)
Open
Source
Based
SM Platform Prototype Architecture
IaaS
Providers
Academic,
Open
Source,
Commercial,
Private
Solutions
SMLC Marketplace Buyer WF Dashboard
Buyer Catalog
Portal Apps
Ecommerce Service Broker
Cloud Formation Orchestration
Cloud Resources
DOE SM UCLA & Nimbis
Kepler Workflow Platform
BACK UP
Smart Manufacturing Metrics Research Plan Outline
• Review relevant past and existing metrics • Choose or define metrics of interest • Define boundaries at each organizational level • Define data needs: inputs and outputs of interest* • Define data sources from data needs based on a mapping • Integrate with existing metrics & Information Management
Systems* • Develop the software with testing, verification & validation* • Develop the front-end interfaces, e.g., the dashboard*
* Based on requirements specifications to be developed
SM Metrics Definition and Goals
• “… developing the components and common definitions for an energy productivity metric will draw upon existing and past metric development efforts…will include basic and complementary indicators, ratios, choice of denominators, e.g., mass, revenue, value add, ‘lower is better’, and benchmarks.
• The basic metric is expected to include (components) raw materials, water, net energy, pollutants, toxics, green-house
gases, and societal metrics.” − DOE Application, 12/7/2011
• Generalize metrics from representative testbeds, beginning with: – continuous (steam reforming at Praxair)
– discrete (forging and machining at General Dynamics).
Primary (direct) Impact Issues
Generic for broad application at Meso level
• Cost • Net Greenhouse Gas reduction vs existing • Additional pollution generated
1. Human and ecological toxicity 2. Degradation of air quality (smog, acid rain precursors, &
particulate)
3. Degradation of aquifers and other potable water supplies (nitrification, silt, BOD/COD, & salt)
4. Degradation of surface waters as ecological habitats
5. Degradation of soil or habitats
AIChE Efforts on Commercial Outreach
Facilitate broader acceptance of the energy productivity metrics by small, medium and large manufacturers (AIChE, NCMS, SMLC) ( 10 to 25% complete)
7.5 (Yr. 3) Establish workshops, webinars, and conferences on SM platform for individual industry segments with emphasis on small and medium size organizations (AIChE, NCMS, SMLC) (10 to 25% complete)
Analog for continuous processes (Praxair)
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