Optimization Strategies
Modern engineering challenges often involve "black-box" systems where gradients are unknown. We specialize in Derivative-Free Optimization (DFO) to navigate these complex landscapes. Depending on the physical reality of the process, we approach the problem with two fundamental methodologies:
Unconstrained Optimization
Searching for the global optimum in a variable space without boundary limitations. Ideal for theoretical system characterization and determining the absolute performance limits of a process.
Constrained Optimization
Solving problems within the boundaries of physical, operational, or safety constraints (e.g., pressure limits, material availability, safety margins). This is the standard for industrial implementation.
Conceptual Framework: DFO
Iterative approach optimizes design variables by evaluating objective function values from black-box models without requiring gradient information.
Core Competencies
- Global Optimization: Identifying the best possible design parameters for complex chemical and mechanical systems.
- Black-Box Simulation Integration: Interfacing with legacy software to extract data and feed optimization loops.
- Custom Algorithm Development: Implementing bespoke heuristic and metaheuristic algorithms tailored to specific process constraints.
- Multi-Objective Optimization: Balancing trade-offs between conflicting goals like cost, yield, and energy consumption.