Process Simulation and Optimization

Lesson 1 • Data Requirements and Sources

Identifies the input data needed to build reliable models and where to obtain it. Poor data quality is the leading cause of inaccurate simulations.

Lesson 2 • Core Modeling Concepts

Introduces mass and energy balances as the mathematical backbone of process models. Connects conservation laws to simulator equations.

Lesson 3 • Simulator Interface and Workflow

Orients students to a generic process simulator environment and standard workflow. Builds confidence before constructing full models.

Lesson 4 • Thermodynamic Property Packages

Explains how simulators calculate physical properties using thermodynamic models. Correct package selection directly affects result accuracy.

Lesson 1 • Heat Exchanger Simulation

Covers rating and design modes for shell-and-tube and plate exchangers. Connects heat duty calculations to overall process energy balance.

Lesson 2 • Separation Unit Operations

Simulates distillation, absorption, and liquid-liquid extraction columns. Rigorous tray-by-tray methods are introduced alongside shortcut approaches.

Lesson 3 • Solids and Multiphase Operations

Extends modeling to crystallizers, dryers, and gas-liquid contactors. Multiphase systems require specialized correlations beyond standard fluid models.

Lesson 4 • Fluid Flow and Pipe Networks

Models pressure drop and flow distribution in piping systems. Accurate hydraulics underpin downstream unit operation performance.

Lesson 1 • Model Validation Techniques

Compares simulation outputs to plant data and design specifications. Validation quantifies model credibility before optimization begins.

Lesson 2 • Convergence Strategies

Addresses common convergence failures and solver selection. Robust convergence is prerequisite to reliable optimization.

Lesson 3 • Recycle and Purge System Modeling

Handles recycle loops with purge streams to prevent inert accumulation. Recycle systems are among the most convergence-challenging flowsheet elements.

Lesson 4 • Steady-State Model Documentation

Establishes standards for recording model assumptions, inputs, and results. Documented models support audits, handovers, and future optimization work.

Lesson 1 • Control Loop Integration

Adds PID controllers and control structures to dynamic flowsheets. Control loops are essential for realistic transient behavior.

Lesson 2 • From Steady-State to Dynamic Models

Explains the mathematical shift from algebraic to differential equations in dynamic mode. Students convert existing steady-state flowsheets to dynamic equivalents.

Lesson 3 • Disturbance and Upset Analysis

Simulates feed disturbances, equipment failures, and utility losses. Results guide control strategy design and safety system sizing.

Lesson 4 • Startup and Shutdown Simulation

Models planned startup and shutdown sequences to identify operational risks. Transient extremes often exceed steady-state design limits.

Lesson 1 • Stochastic and Global Optimization

Introduces metaheuristic and stochastic methods for non-convex problems. These methods escape local optima where gradient-based solvers stall.

Lesson 2 • Mixed-Integer Programming

Handles discrete decisions such as equipment selection and process topology. MILP and MINLP extend continuous optimization to structural choices.

Lesson 3 • Sensitivity and Parametric Analysis

Quantifies how optimal solutions change with parameter variations. Sensitivity analysis reveals which constraints and parameters most influence the optimum.

Lesson 4 • Optimization Problem Structure

Defines objective functions, decision variables, and constraints in engineering context. Correct problem formulation determines whether a solver can find a useful solution.

Lesson 1 • Multi-Objective Optimization

Balances competing objectives such as cost versus environmental impact. Pareto front generation reveals trade-offs for informed decision-making.

Lesson 2 • Optimization Under Uncertainty

Incorporates feed variability and parameter uncertainty into optimization. Robust and stochastic programming produce solutions that perform well across scenarios.

Lesson 3 • Constraint Handling in Process Optimization

Manages safety, quality, and capacity constraints during optimization runs. Infeasible solutions must be detected and handled without aborting the solver.

Lesson 4 • Single-Objective Process Optimization

Optimizes one objective such as minimum energy or maximum yield across a full flowsheet. Students apply NLP solvers within the simulator environment.

Lesson 1 • Retrofit and Debottlenecking

Applies energy integration methods to existing plants with fixed equipment. Retrofit differs from grassroots design by imposing structural constraints.

Lesson 2 • Mathematical Programming for HEN

Formulates heat exchanger network synthesis as a MILP or MINLP problem. Mathematical methods find globally optimal networks beyond heuristic reach.

Lesson 3 • Utility System Optimization

Optimizes steam, cooling water, and refrigeration systems alongside the process. Utility system design significantly affects total site energy cost.

Lesson 4 • Pinch Analysis Methodology

Uses composite curves and the pinch point to set minimum utility targets. Pinch analysis is the industry-standard starting point for energy integration.

Lesson 1 • Sustainability and Emissions Optimization

Incorporates carbon footprint, waste, and water metrics into optimization objectives. Sustainability constraints are increasingly mandatory in industrial practice.

Lesson 2 • Capstone Industrial Case Studies

Applies all course skills to full-scale industrial problems in refining, chemicals, and utilities. Students present solutions with economic and technical justification.

Lesson 3 • Real-Time Optimization Systems

Integrates online plant data with steady-state models for continuous optimization. RTO systems close the loop between simulation and plant operation.

Lesson 4 • Model Predictive Control Integration

Connects dynamic simulation models to MPC controller design and testing. MPC relies on accurate dynamic models to predict and optimize future behavior.