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Industrial Algorithms
 

Industrial Algorithms

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    Industrial Algorithms Industrial Algorithms Presentation Transcript

    • Introduction Industrial Algorithms LLC. Jeff Kelly & Alkis Vazacopoulos July 18, 2013
    • Who we are? – Jeff Kelly: 25-years of both production & process modeling & optimization for planning, scheduling, control & estimation (PSCE) problems in the process industries, worked in Shell, Exxon, Honeywell, consulted for more than 30 companies. – Alkis Vazacopoulos: 25-years of solving production planning and scheduling in process, printing & publishing, consumers goods, etc, worked for Dash Optimization, Fair Isaac, Verisk and consulted for more than 100 companies. 7/19/2013Copyright, Industrial Algorithms LLC
    • Our Mission • To provide efficient solutions to solve complex APS (Advanced Planning and Scheduling) problems. • Our primary focus is to implement smaller applications with large benefits versus installing a large application with small benefits. To do this, we help you identify your worst decision- making bottlenecks and provide solutions targeted to reducing their negative impact on your bottom-line. 3
    • 4 What We Do • iAL develops and markets iMPress, the world’s leading software product for flowsheet modeling and optimization. • iAL provides in-house training for customers along with complete software support and consulting. • iAL provides Industrial Modeling Frameworks (iMf’s) for several problem types.
    • Our Mandate • To provide advanced modeling and solving tools for developing industrial applications in the decision-making and data-mining areas. • Our targets are: – Operating companies in the process industries. – Application software providers. – Consulting service providers.
    • Our Industrial Modeling Frameworks • Process industry business problems can be complex hence an Industrial Modeling Framework provides a pre-project or pre- problem advantage. • An iMf embeds Intellectual Property related to the process’s flowsheet modeling as well as its problem-solving methodology.
    • What type of iMFs we have developed • Jet Fuel Supply Chain Design with Refinery and multimodal transportation mode • Maritime Shipping Supply Chain Design • Real Time Blend Optimization • Pipeline Scheduling Optimization • Fast Moving Consumer goods – Planuling Optimization • Capital Investment & Facilities Location • Advanced Production Accounting • Advanced Process Monitoring • Advanced Property Tracking/Tracing 7
    • Our Business Model • What do we license: iMPress, iMf, 3rd Party Solvers. • What is our pricing scheme: License Fee, Support & Maintenance Fee. • What are our license terms: Based on Customer’s Needs i.e., Rental for a specified period (months to years) or Perpetual.
    • 9 iAL Services • Application Support – Free prototyping to get you started (for a reasonable number of consulting hours). – Full modeling and solving support. • Consulting Services
    • 10 iAL Facts • Founded 2012 • Offices in the US (New Jersey), Canada (Toronto)
    • 11 Industrial Algorithms Differentiators • Modeling Focus • Optimization Focus • No Competition with Consultants and OEMs • Customer Support • Solution of Hard Problems • Flexible Licensing Terms
    • Academic Collaboration & Partnership • Carnegie Mellon University • University of Wisconsin • Stevens Institute Of Tech • Fairleigh Dickinson University • George Washington University
    • 13 Academic Partnership Program • Free Full-Edition iMPress licenses for degree- awarding institutions for research and teaching
    • 14 Working with Customers – Current Projects • Pipeline Optimization with DRA. • Refinery Planning and Scheduling. • Fast Moving Food Industry Planning and Scheduling. • Jet Fuel Supply Chain. • Beer Supply Chain Planning and Scheduling. • Gasoline Blend Monitoring with ProSensus. • Data Reconciliation Engine embedded in TUVienna STAN Software.
    • 15 Development Directions Performance Ease of Use Problem Types
    • 16 Development Directions Performance Ease of Use Problem Types
    • We solve problems that deal with the following decisions: Quantity How much to produce? What is the batch-size? Quality How to blend specific products to satisfy certain levels of quality? Logic What machines to use? How to sequence the jobs to minimize setup costs? Time When to produce? How to respect past decisions & future orders? 7/19/2013 17 Copyright, Industrial Algorithms LLC
    • We solve these types of problems Our system can model and solve problems which are a mix of both planning & scheduling decision-making. We introduce nonlinear optimization in large-scale planning and scheduling problems and solve problems involving quantity, logic & quality. We properly manage complexity in problems that would normally be considered as uncertainty by other vendors. We use data-mining techniques to support the solving of problems that incorporate control, feedback, and maintainability issues. 7/19/2013 18 Copyright, Industrial Algorithms LLC
    • 19 Development Directions Performance Ease of Use Problem Types
    • How do we model the Superstructure?  Configure versus Code:  Draw the flowsheet of connected industrial objects and the sets, parameters, variables, constraints & derivatives are automatically created.  User, custom or adhoc sub-models can also be coded when required. Unit- Operation 1 Unit- Operation 2 Port-State 1 Port-State 2 charge, batch & lot-sizing, input-output yields, stream flow bounding, min/max run-lengths & cycle-times, sequence-dependent setups, certification delays, density, composition & property limits, nonlinear & discontinuous formulas, economic, environmental & efficiency objectives, etc.
    • Why are we unique? • iMPress is flowsheet-based (i.e., a figurative language). – This means that the modeling is inherently network or superstructure “aware” with equipment-to- equipment, resource-to-resource, activity-to-activity, etc. as explicit language constructs or objects. – It also means that all of the effort of generating the sparse A matrix in the LP, MILP and NLP is done automatically by automatically creating all of the sets, parameters, variables and constraints when the model is configured using our proprietary and comprehensive library of sub-models. 7/19/2013 21 Copyright, Industrial Algorithms LLC
    • Why are we unique? • iMPress is “shape-based” which is different from other modeling systems: – Algebraic modeling languages like GAMS, AIMMS, AMPL, etc. are “set-based”. – Applied engineering modeling languages like ACM, gPROMS, APMonitor, NOVA-MS, Modelica, etc. are “structure-based”. – Array manipulation modeling languages like Matlab, Mathematica, Octave, etc. are “scalar-based”.
    • Jet Fuel Supply Chain iMf
    • How do you configure problems? • Problems are configured either: – Interfacing with our flat-file Industrial Modeling Language (IML) or – Interactively with our Industrial Programming Language (IPL) using a programming language such as C, C++, C#, Java, Python, etc. 7/19/2013Copyright, Industrial Algorithms LLC
    • 25 Development Directions Performance Ease of Use Problem Types
    • Performance Issues • Solve Large-Scale Problems. • Take Advantage of MILP technologies and multiprocessors. • Efficient Memory Management. • Strong Presolving. • Nonlinear Technology that can handle complex NLP problems. • Support for decomposition/polylithic modeling. 26
    • What Math Programming and Solvers we use? Supply-chain planning and scheduling optimization problems, Logistics modeling and solving is required utilizing Mixed-Integer Linear Programming (MILP). Production-chain planning and scheduling optimization problems, both Logistics and Quality optimization models are solved using an integrated and innovative combination of both MILP and Nonlinear Programming (NLP). We currently have bindings to several linear and nonlinear programming solvers such as COINMP, GLPK, LPSOLVE, SCIP, CPLEX, GUROBI, XPRESS, XPRESS-SLP, CONOPT, IPOPT, KNITRO & IMPRESS-SLPQPE. 7/19/2013 27 Copyright, Industrial Algorithms LLC
    • Real Time Blend Optimization iMf 28
    • Fast Moving Consumer Goods iMf Bulk-Line Pack-Line Sequence- Dependent Switchovers Forecasted & Firm Future Demand Orders
    • • Time Horizon: 60 time-periods w/ day periods. • Continuous Variables = 10,000 • Binary Variables = 5,000 • Constraints = 20,000 • Time to First Good Solution = 10 to 30- seconds • Time to Provably Optimal = 1 to 10-hours due to sequence-dependent switchovers. • Solver: Tested with Xpress & Gurobi 7/19/2013Copyright, Industrial Algorithms LLC Fast Moving Consumer Goods iMf
    • Cogeneration (Steam/Power) iMf 7/19/2013 Copyright, Industrial Algorithms LLC • Time Horizon: 168 time-periods w/ hour periods. • Continuous Variables = 5,000 • Binary Variables = 1,000 • Constraints = 7,500 • Time to First Good Solution = 5 to 30- seconds • Time to Provably Optimal = 5 to 15-minutes
    • Cogeneration (Steam/Power) iMf Water Pump
    • • Time Horizon: 168 time-periods w/ hour periods. • Continuous Variables = 5,000 • Binary Variables = 1,000 • Constraints = 7,500 • Time to First Good Solution = 5 to 30-seconds • Time to Provably Optimal = 5 to 15-minutes. • Solver: CPLEX 7/19/2013Copyright, Industrial Algorithms LLC Cogeneration (Steam/Power) iMf
    • Power Generation iMf • Three thermal-plants and two hydro-plants with and without water storage. • Three nodes or buses with voltage phase angle inputs where each bus obeys Kirchhoff’s current and voltage laws. • One time-varying demand load located on bus #3.
    • Power Generation iMf
    • Capital Investment/Facilities Location iMf Expansion? Installation?
    • Maritime Industrial Shipping iMf Inventory Routing
    • SubsTance flow ANalysis (STAN) iMf • Large-scale data reconciliation and regression is performed to compute observability, redundancy and variability estimates. • Substances are any material or meta/sub-material (concentrations) which need to be traced within the flowsheet or network to track their movements based on flow and composition measurements over time. • STAN is a software development from TUVienna using iAL’s iMPress solver called SECQPE (successive equality-constrained QP engine). 7/19/2013Copyright, Industrial Algorithms LLC
    • 7/19/2013 Copyright, Industrial Algorithms LLC SubsTance flow ANalysis (STAN) iMf
    • Other uses of IMPRESS … • First-principles or rigorous process modeling to manage difficult but high-valued bottlenecks. • On-line process/production monitoring to compare model predictions with plant actuals in real-time. • Large-scale nonlinear optimization to solve industrial scale problems where there is a large portion of linear constraints and a smaller portion of nonlinear constraints with multilinear cross-product terms (x1*x2) using successive linear & quadratic programming. 7/19/2013Copyright, Industrial Algorithms LLC
    • Linking iMPress with IBM/ILOG ODME & Cplex (work with DecisionBrain) System Architecture
    • ODME-IMPRESS-CPLEX System Architecture • A domain-specific data model was created in ODME using the usual master-data and transactional-data partitions. • A mapping between iMPress’ data model and ODME’s data model was established. • Java code was written to export iMPress’ IML file (Industrial Modeling Language). • SWIG Java was used to create a Java Native Inerface (JNI) to iMPress.
    • ODME-IMPRESS-CPLEX System Architecture • Java code was written to call iMPress-CPLEX using its API’s. • Java code was written to access the solution(s) from iMPress-CPLEX using its API’s and to populate the ODME solution-data partition.
    • ODME Screen Shots
    • Data-Model in ODME
    • Master-Data
    • Transactional-Data
    • Gantt Chart for Reference (Base)
    • Trend Plots for Reference (Base)
    • Demand Variability Scenario Data w/ Reference in ()
    • Trend Plots for Demand Variability Scenario w/ Reference
    • Benefits • Perfectly fit your business model and decision processes • Sophisticated optimization capabilities able to tackle complex, non-linear and large-scale problems • A solution that can be quickly adapted to new production processes • A user-friendly GUI to help planners driving refinery operational excellence and analyzing refinery behavior • What-if scenario analysis for confident decision-making • See all your data and options in one place with drill-downs and graphics • Collaborate with other planners • Powered by IBM ILOG CPLEX Optimizers
    • How do we engage? • We first consult to determine how we can improve the profit and performance of the problem as a whole. • Then, depending on the benefit areas and apparent bottlenecks, a tailored and incremental solution is implemented which focuses on both improving economics and increasing efficiency whilst being transparent and usable. 7/19/2013 53 Copyright, Industrial Algorithms LLC
    • How do we engage? • Using our Industrial Modeling Frameworks (IMF): These are preconfigured solutions that we can adopt to your specific problems. • We have IMFs in the following areas: – Production Planning – Plant Scheduling – Pipeline & Marine Shipping – Energy Management 7/19/2013 54 Copyright, Industrial Algorithms LLC
    • For a demonstration of our IMFs & IMPRESS, please Contact • Alkis Vazacopoulos • Industrial Algorithms LLC • Mobile: 201-256-7323 • alkis@industrialgorithms.com 7/19/2013 55 Copyright, Industrial Algorithms LLC