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Untangle Those Loops!Solving Process and Control Interactions

Steve ObermannExperTune, Inc.

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Presenter

• Steve Obermann graduated from Lafayette College in Pennsylvania with a degree in chemical engineering, and has worked in the process automation field for more than 25 years. He has extensive experience in the refining and petrochemicals sector; having been employed by Texaco, WR Grace, UOP, Honeywell, and Texas Petrochemicals. His expertise includes process modeling, advance control, optimization, software development, process and control system performance evaluation/benchmarking, project financial analysis and management. Steve is an applications specialist with ExperTune Inc.

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AbstractUntangle Those Loops!• Process plants are full of complex interactions, making it difficult to change one area of the plant

without affecting another. In fact, it is quite common for a single change or upset to have a ripple effect on dozens of other controllers. This presentation shares several case studies showing effective techniques to stabilize the plant, with a minimum of changes throughout the plant.

• Process interactions come about for a variety of reasons: process design, heat integration, recycle streams, and control strategies. To truly optimize a process plant, you must be able to understand the interactions, and to identify the original source of variability.

• Thankfully, most plants have access to huge warehouses of real-time data. This operating data can be used to pinpoint the original sources of variability. However, few are actually leveraging that data to develop process improvements. This paper illustrates specific techniques to take advantage of process data, with a focus on practical, real-world diagnostics.

• This paper addresses the science and the practical application of modern mathematical process analysis, including: oscillation detection, interaction analysis, and automated process modeling. The tutorial presentation will also provide case studies showing how these approaches have been applied in process plants to deliver millions of dollars in savings.

• Most process plants have significant opportunity to improve performance. Energy cost, quality, and production rate are greatly affected by process upsets that start in a remote area of the plant, but propagate into the mainstream. Finding the root cause of process upsets has always been difficult. However, over the past five years, the real-time controller performance monitoring has led to breakthroughs in practical root-cause problem-solving.

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Agenda

What Are Process Interactions?

Finding Interactions

Oscillation Detection

Root Cause

Conclusions, Q&A

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What are Process Interactions?

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Agenda

What Are Process Interactions?

Finding Interactions

Oscillation Detection

Root Cause

Conclusions, Q&A

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Sources of Interactions

• Process Design– Recycle Loops– Energy Conservation– Common Headers

• Control Design– Ratio Control

• Operating Procedures– Bypass– Re-Blend– Blow-down

• Equipment Issues

Cross-Correlation

• Interaction between parts of the system

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Agenda

What Are Process Interactions?

Finding Interactions

Oscillation Detection

Root Cause

Conclusions, Q&A

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Process & Control Oscillations

• Oscillation Period– Fourier Transform

• Oscillation Shape

• Focus on Root Cause

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Sorting Oscillations by Period

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Agenda

What Are Process Interactions?

Finding Interactions

Oscillation Detection

Root Cause

Conclusions, Q&A

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Finding Root Cause

• Process vs. Instrument vs. Valve vs. Control

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Measure Decide Action

…in Real Time

Other Tips:Plotting PV vs. CO

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Good Oscillating

Hysteresis Stiction

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Interaction Hot Spots

Process Interaction Map

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Examples

SABIC Saves $1 Million/Year in Energy Costs

**Company Name Withheld** Saves $500,000/year in one afternoon

Typical Results

• Energy Savings 0.5% to 2%• Production increase 1-10%• Reduced Maintenance Cost• Reduced Waste/Scrap• Return on Investment

– Typical 2-3 months

– Often within days

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Agenda

What Are Process Interactions?

Finding Interactions

Oscillation Detection

Root Cause

Conclusions, Q&A

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Conclusions

1. Interactions can be Identified from Existing Data2. Oscillation Detection Can Identify Likely Source

a. Use Oscillation Period

b. Pay Attention to Wave Shape

3. Root Cause Interaction Analysisa. Immediate Solution to Difficult Problems

b. Process, Control, Procedures, or Equipment

4. Root Cause Methods are Fast & Efficient.a. Find one, fix many.

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21Thank You!