| Educational guide School of Chemical Engineering |
english |
| Chemical Engineering (2013) |
 Subjects |
| PRODUCT AND PROCESS DESIGN I |
| Contents |
| IDENTIFYING DATA | 2022_23 |
| Subject | PRODUCT AND PROCESS DESIGN I | Code | 20695105 | |||||
| Study programme |
|
Cycle | 2nd | |||||
| Descriptors | Credits | Type | Year | Period | ||||
| 4.5 | Compulsory | First | 1Q |
|||||
| Competences | Learning outcomes | Contents |
| Planning | Methodologies | Personalized attention |
| Assessment | Sources of information | Recommendations |
| Topic | Sub-topic |
| Fundamentals of product and process design. |
- Why modelling? - Fundamentals of optimization |
| Product and process design using mathematical programming & optimization. Decision support tool: solver MSExcel | - Easy things I have to say - Solver Excel Training: toy examples - Practical issues - Examples: toy examples, power generation, allocation (with carryover), cutting pieces, 2 echelons supply chain, 3 echelons supply chain, multiproduct supply chain, facility closing, HHRR scheduling, HHRR scheduling with workers preference, process synthesis (superestructures), dynamic optimization, multiobjective optimization - Advanced aspects |
| Product and process design of batch processes |
- Overview of batch processes: Operation mode, up & down streams, Gantt chart - Scheduling of single product plants: Non-overlapping & overlapping - Scheduling of multiple products plants: Flow/jobshop, product campaigns (single and mixed), transfer policies (Zero wait, no-intermediate storage tank & unlimited intermediate storage tank) - Design and retrofit batch processes: Beer batch production -Paper and pencil case studies. - Lessons learnt |
| Batch product and process design. Decision support tool: SuperPro | - Modelling batch processes: decision making. - Super Pro minitutorial. Case studies. - SuperPro workshops. - Case studies: beta galactose, WWTP, SPD, continuos, cost analysis, databanks, MAB, brewery, carrageeenan, corn refinery, WFI. |
| Product and proces design: Optimization | General concepts and NLP - Introduction to optimization - Convexity of an optimization problem - Karush-Kuhn Tucker conditions of an optimization problem Linear Programming: - Introduction to linear programming - Sensitivity analysis in LP - How to solve simple LPs in Excel MILP & MINLP: - Fundamentals of MILP - Branch and bound method - Fundamentals of MINLP |
