Optimized process design (Efftech/POJo)

Transcription

1 Optimized process design (Efftech/POJo) Risto Ritala POJo Project Coordinator, TUT SHOK Summit

2 Content of the presentation 1. Goals and approach Conceptual design: multiobjective and concurrent design of process structure, dynamics and operation 2. General methodology illustrated through a case Furnish management system at a paper mill 3. The energy efficiency aspect in POJo 4. Next steps 5. Conclusions 2

3 1. Goals and approach POJo main goal: develop a methodology for production system design that takes full benefit of information-related degrees of freedom to improve tradeoff possibilities in Net Present Value (capital intensiveness), energy consumption, environmental impact Technical goals: formulate the design as a multi-objective optimization problem on a super-structure dynamically modeled Library of models (paper industry related and general) Opportunities of IT infrastructure in design process and solutions Value of operational information and control Role of uncertainty life cycle effects and value of flexibility Demonstrate the methodology in a conceptual design case study Furnish system in SC paper production 3

4 1. Goals and approach: concurrent design of structure and operation/dynamics Methodology of optimal design; formulated objectives constraints and scenarios as starting point Evaluate the multiobjective performance and constraints Assess and modify Definition of a production system concept: 1) Process structure 2) Modes of operation 3) Structures and parameters for information system, decision support control and measurements Dynamic optimization of concept operation: 1) Automated actions 2) Operator actions 3) Production planning 4) Make a model 4

5 1. Goals and approach: multiple objectives both in design and operation Multiple objectives arise naturally at both design and operation (the very reason for having decision makers!) Economic performance/quality/energy/environmental impact/safety/.. Decisions are about choosing tradeoffs amongst Pareto optimal solutions Pareto optimal: cannot improve with respect to one objective without giving up performance with respect to at least one of the other objectives Dilemma when designing mode of operation: no operative decision maker present (research problem) Now: design a single objective operational problem In future: design a set of single objective problems to be chosen amongst when the actual plant is operated 5

6 2. Case study within SC paper production: definition TMP DILC Pulpers DILC Balance Water (surplus/deficit) Pure water Clean water Fresh water TMP DIL1 Chem pulp DIL1 Recovered solids Filler Separation=DF 0-water Fresh water MIXING volume 3.5 % consistency DIL2 PM 3.0 % consistency DIL1 Paper, brutto Quality control Paper, net Broke (deficit/ surplus) DIL1 Dry broke storage 6

7 2. Features System simplifications you typically need to do these: Design: Single grade Ideal disc filter -> two flow fractions only Mostly ideal consistency control (non-ideal filler/bw control) To be chosen optimally: Tower volumes, control structures (objective function parameters), measurements To be optimized: Investment cost, operational cost, risk for volume overflows, quality variations Operational (decision interval: 10 min or longer) To be chosen optimally: flows, flow ratios, (consistency setpoints) To be optimized: risk for breaks, risk for overflows, quality variations 7

8 2. In operation user has the following controls available Accessible: Broke dosage TMP to CP in mixing TMP production rate CP production rate DF input flow Rate of pulping dry broke Intake of fresh water TMP top cons CP top cons Recirculation flow at DF Not accessible CP, TMP bottom consistencies (controlled, ideal) Broke consistencies (controlled, ideal) Approach consistency (controlled, ideal) Basis weight set point (determined by recipe) Filler content set point (determined by recipe) 8

9 2. Break models uncertainty and uncertainty about the uncertainty Breaks and recoveries from break are stochastic events Break model 1: Break risk increases as broke dosage increases Recovery from break is independent of broke dosage Break model 2: Break risk and recovery probability depend on web strength Web strength is determined by material composition Increasing broke dosage, increases the amounts of fillers and non-virgin TMP -> strength decreases -> risk for break increases Due to high uncertainty in break risk models, the robustness of results will be carefully analyzed 9

10 2. Case studies within SC paper production: definition CASE 0 + BREAK MODEL 1 (solved) TMP DILC Pulpers DILC Balance Water (surplus/deficit) Pure water Clean water Fresh water TMP DIL1 Chem pulp DIL1 Recovered solids Filler Separation=DF 0-water Fresh water MIXING volume 3.5 % consistency DIL2 PM 3.0 % consistency DIL1 Paper, brutto Quality control Paper, net Broke (deficit/ surplus) DIL1 Dry broke storage 10

11 2. Case 0: typical operation when optimized VOLUME BREAKS DOSAGE FILLER: DEVIATION FROM TARGET 11

12 2. Case 0 formulation for operation: from multiple objectives to single -> parameters to be designed subject to Objective Filler model Break model Volume model and overflow constraint 12

13 2. Case 0: design formulation Objectives: V max s. t. V min,,, p0 max ( m) H ( V max E T E q1 E c 2 f ) of E ( u( n 0 1) u( n)) 2 - Capital expenditure - Lost production time due to insufficient buffering capacity - Lost production time due to web breaks - Uniformity of quality - (Smoothness of operation) Degrees of freedom - Volume(s) - Parameters of operational objective 13

14 2. Case 0: assessing a design perfromance For a fixed design d, set s = 1 Fix the initial states of the simulator If s S Simulator (process model) - apply u*(1) - update states -if overflow, stop n u*(1) Operational optimizer - solve QP problem to obtain u*(1)...u*(k) If s >S Calculate the means of - overflow - filler content variation -time within breaks 14

15 2. Case 0 tradeoffs: presentation and selection with four objectives Each point associated with fixed values of design degrees of freedom (128 design alternatives). Life time performance estimated by repeated simulation. 15

16 2. Case 0: robustness Break probability Overflow time Filler variation Time in breaks True/model High/ High High/ Med High/ Low Med/ High Med/ Med Med/ Low Low/ High Low/ Med Low/ Low

17 3. The energy efficiency aspect in POJo The case studied did not involve energy efficiency directly BUT: POJo has been developing a methodology Energy efficiency related objectives can be included in straight forward manner (you need the corresponding process models, of course) Discounting of non-economic performance measures? Uncertainty about price of energy over the plant life time can be included (another model) Uncertainty analysis typically increases the number of objectives (risk premiums or other formulations) Value of flexibility In Efftech 2 energy efficiency metrics will be of high importance: design of entire production system concepts 17

18 4. Next steps Methodological development Be able to solve increasingly complicated design problems (scope, degrees of freedom, details in system models) Solutions that will delegate more decision making to operational level Restore the multi-objective nature of operations, yet guaranteeing Pareto optimality with respect to design Increased flexibility to cope with business environment developing differently to what was anticipated during design Continuous redesign of operational mode as information about evolvement of the business environment accumulates? Case analyses Case presented above solved in a wider scope by 6/10 Entire production system concepts to be studied in Efftech2 18

19 5. Conclusions The POJo project has been developing a new and generic approach to conceptual design Optimized in multi-objective sense and model based Concurrent in design of process structure, mode of operation, control and measurements Approach is generic, not forest industry specific, but requires domain-specific process models Methodology will be developed further in Efftech 2 Working practices related to the new methodology are also studied in POJo Case approach: from simple cases to increasingly complicated ones In Efftech 2 the conceptual optimization of novel lean and efficient production systems will be based on POJo approach In future case studies objectives related to energy efficiency both in operational and design are foreseen to be of high importance 19

20 THANK YOU With international cooperation Prof E.N. Pistikopoulos, Imperial College (POJo researcher Aino Ropponen visited 5/09-3/10) Prof. Margaret Wiecek, Clemson University (visiting Jyväskylä) 20