Real-time optimization revolutionizes chemical processes by continuously fine-tuning operations. It identifies key variables, defines objectives, and develops mathematical models to maximize efficiency and profitability while respecting constraints.

Integrating optimization with control creates a powerful system for process improvement. By aligning economic goals with performance objectives, real-time optimization delivers tangible benefits like reduced energy consumption, increased product yield, and enhanced flexibility to meet market demands.

Real-Time Optimization Fundamentals

Optimization for process improvement

• Identify key process variables and constraints
  • Manipulated variables (MVs) directly influence the process behavior and are adjusted by the optimizer (flow rates, temperatures, pressures)
  • Controlled variables (CVs) are process outputs that need to be maintained at desired setpoints or within acceptable ranges (product quality, safety limits)
  • Disturbance variables (DVs) are uncontrolled inputs that affect the process performance (feed composition, ambient conditions)
  • Equality constraints represent physical laws or material balances that must be satisfied (mass and energy balances)
  • Inequality constraints define operational limits or safety boundaries (equipment capacities, environmental regulations)
• Define objective functions for process optimization
  • Economic objectives aim to maximize profitability by minimizing costs (raw materials, energy) or maximizing revenue (product sales)
  • Performance objectives focus on improving process efficiency, such as minimizing energy consumption or maximizing product yield
• Develop mathematical models for process optimization
  • Steady-state models describe the process behavior at equilibrium conditions and are used for static optimization (mass and energy balances, thermodynamic relations)
  • Dynamic models capture the time-dependent behavior of the process and are used for dynamic optimization (differential equations, transfer functions)
  • Data-driven models leverage historical process data to build predictive models using machine learning techniques (neural networks for nonlinear relationships, support vector machines for classification)

Strategies for chemical process optimization

• Steady-state optimization
  • Nonlinear programming (NLP) techniques solve optimization problems with nonlinear objective functions and constraints
    • Sequential quadratic programming (SQP) iteratively solves a series of quadratic programming subproblems to find the optimal solution
    • Generalized reduced gradient (GRG) method transforms the constrained problem into an unconstrained one using variable substitution
  • Linear programming (LP) techniques are used when the objective function and constraints are linear
    • Simplex method systematically explores the vertices of the feasible region to find the optimal solution
    • Interior-point methods traverse the interior of the feasible region to reach the optimal solution
• Dynamic optimization
  • Dynamic programming (DP) breaks down the optimization problem into smaller subproblems and solves them recursively (Bellman's principle of optimality)
  • Nonlinear model predictive control (NMPC) optimizes the process behavior over a future time horizon using a dynamic model
    1. Sequential approach optimizes the control actions at each sampling time, considering the current state and future predictions
    2. Simultaneous approach optimizes the control actions and state trajectories simultaneously over the entire horizon
  • Evolutionary algorithms mimic natural selection to explore the solution space and find near-optimal solutions (genetic algorithms evolve a population of solutions, particle swarm optimization guides the search using swarm intelligence)

Real-Time Optimization Integration and Benefits

Integration of optimization and control

• Hierarchical control structure
  1. Real-time optimization (RTO) layer periodically updates the optimal operating points based on the current process conditions and economic objectives
  2. Model predictive control (MPC) layer tracks the optimal setpoints provided by the RTO layer while respecting process constraints and dynamics
  3. Regulatory control layer maintains the process variables at their desired setpoints using conventional feedback control loops (PID controllers)
• Data exchange between RTO and MPC
  • Setpoint updates from RTO to MPC ensure that the MPC layer operates the process at the economically optimal conditions determined by the RTO layer
  • Process model updates from MPC to RTO provide accurate and up-to-date information about the process behavior for reliable optimization
• Coordination of RTO and MPC objectives
  • Alignment of economic and performance objectives ensures that the RTO and MPC layers work towards the same goals (maximizing profit while meeting product quality and safety requirements)
  • Handling of constraint violations and infeasibilities requires robust optimization algorithms and fallback strategies to maintain process stability and safety

Economic impact of real-time optimization

• Improved process efficiency
  • Reduced energy consumption by optimizing operating conditions (temperatures, pressures) and minimizing waste heat
  • Increased product yield by maximizing the conversion of raw materials into desired products and minimizing side reactions
  • Minimized raw material usage by optimizing feed rates and compositions based on real-time process measurements
• Enhanced process flexibility
  • Faster response to market demands by adjusting production rates and product grades according to customer requirements
  • Ability to handle varying feedstock quality by adapting the process conditions to maintain product specifications
• Increased profitability
  • Reduced operating costs by minimizing energy consumption, raw material usage, and waste generation
  • Increased revenue through higher production rates and improved product quality
  • Minimized off-spec product generation by continuously monitoring and optimizing the process performance
• Case studies and industrial applications demonstrate the tangible benefits of real-time optimization
  • Petrochemical plants have reported significant energy savings and increased production capacity (ethylene, propylene)
  • Refineries have achieved higher yields and improved product quality by optimizing the crude oil processing units (crude distillation, fluid catalytic cracking)
  • Power generation facilities have reduced fuel consumption and emissions by optimizing the combustion process and heat recovery systems (boilers, turbines)