Automation enhancement is a multifaceted endeavor that extends beyond closed-loop control systems. While closed-loop control forms the core, numerous opportunities exist for on-site improvements. Our team has been utilizing frequency adjustment programs to minimize manual intervention by operators in adjusting variable frequency drives (VFDs). However, recent input from on-site engineers revealed that configuring ramp-up logic directly within the VFD can achieve the same goal with greater simplicity and efficiency. This approach exemplifies how cross-departmental collaboration can lead to more fundamental and optimized solutions.
The Core Components of Automation Enhancement
Effective automation enhancement involves refining four critical components: the process object, sensing, decision-making, and execution. The adoption of closed-loop feedback control aims to introduce complexity strategically to mitigate uncertainty under constraint conditions. Feedback mechanisms are the most effective means to handle open, complex, dynamic, and uncertain environments. They not only improve the performance of the original system but also provide controllability to inherently uncertain systems.
Sensing: The Eyes of the System
Accurate and pervasive sensing is essential for identifying and addressing system issues. The integration of additional detection instruments is often a necessary step. Fortunately, the decreasing costs of control assets have made it feasible to deploy more sensing instruments compared to actuators. Building on existing sensors, designing controlled variables aligns with the core concept of soft measurement, which processes secondary instrument data to create actionable insights. Simple logical operations on process variables can significantly simplify complex issues.
Decision-Making: Leveraging Control Algorithms
In the decision-making phase, sophisticated algorithms are not always the priority. Trusting the capabilities of PID controllers, mastering tuning methods, understanding the fundamental nature of problems, and utilizing appropriate control structures are critical. The breakthrough in control algorithms lies in identifying control challenges where PID is inadequate but still relevant to engineering applications. Striving for optimal system performance under constraints is key, as deviation-based extreme performance has limited practical significance. In process control, PID remains indispensable, and advanced controls should complement rather than replace it to handle greater uncertainties.
Execution: Reducing Nonlinearities for Precision
Minimizing nonlinearities in the execution phase and ensuring precise control implementation are pivotal for automation advancement. Execution nonlinearity poses significant control challenges. For example, incorporating valve positioners with control valves is a feedback mechanism that improves the characteristics of the controlled object. When designing control solutions, adopting hierarchical cascade control strategies can further enhance object performance.
Improving Object Characteristics
Enhancing object characteristics can dramatically boost control performance, although it depends on constraints, conditions, and available means, making it challenging to achieve consistently. Utilizing various operational methods simultaneously to meet control requirements can increase the complexity of achieving optimal performance. Improvement in object characteristics often results in substantial gains but can be difficult to replicate.
Strategies for Effective Automation Enhancement
Automation enhancement requires a comprehensive approach that involves problem definition and acknowledgment as the first steps. Analyzing operations to reduce manual interventions should focus on utilizing mature technologies to achieve optimal performance in the simplest form under given constraints. Cross-departmental brainstorming and collaboration are essential for identifying and implementing the most effective solutions.
Conclusion
In conclusion, successful automation enhancement hinges on systematically improving sensing, decision-making, and execution while leveraging existing control frameworks like PID. Emphasizing cross-functional teamwork and adopting simplified yet effective solutions can lead to significant advancements. By thoughtfully integrating feedback mechanisms, refining process variables, and reducing execution nonlinearities, organizations can achieve superior automation performance and resilience in complex, uncertain environments.