How do you evaluate your plant operations simulation impact?

prior to running a simulation in process plant operation, it is necessary to observe the detailed objective that would be the final outcome of the process simulation. For instance, process simulation will generate added value and some valuable insight into the existing process and leading to process efficiency, reliability and sustainability production

When we look at defining objectives to drive operational excellence, it should focus on one or combination of the following: - Improved efficiency, - Reduced downtime, - Increased throughput or - Improved product quality.

Identify key performance indicators (KPIs) related to your objectives. Measure and compare them against your current situation. Baseline comparison evaluate simulation results by comparing them to a baseline or existing performance. Realistic targets set achievable targets for each KPI. Remember, well-defined objectives pave the way for meaningful simulations.

Once the detailed quantitative objectives of the process simulation are finalized, the collective method of the input data of process simulation is the next important step. In the now days, collecting the actual data from the process plant feed is easy to access and conducted since there are a a lot of smart sensors have been fabricated by manufactured. We can easily extract or download it as raw data (txt, XML, xls, csv) and then convert it into visualized data that shows some of trend or insight. We can use python, Excel, Power BI, MatLab to conduct all the data analytics tool to see the data trend.

Collecting historical data from your plant operations before implementing the simulation. This data will serve as a baseline against which you can compare the simulation results. Data validation and reconciliation plays a big part in sanity of the simulation model developed and its results.

Look for patterns and trends in the data. Identify any significant differences between simulation results and actual plant performance. These insights can guide decision-making and process improvements. Remember that a thorough analysis requires not only technical expertise but also a deep understanding of the specific context and domain. It’s essential to interpret the results in light of practical implications for your plant operations.

In recent years, hybrid simulation has been one of the popular process simulation model techniques to perform the simulation accurately with the actual plant data from the field. The hybrid model combines the two essential parameters. First, we can call it as 1st principle. it's the basic theoretical knowledge (thermodynamics, mechanical fluid, energy, and mass balance). the second one is the actual data itself. In some ways, we can make a comparison diagram within the process model data and the field of actual completed with the error value between them. once, your process model has less error value comparing with tolerable error criteria, then you can utilize your process model to forecast or predict the other process parameter

Run the simulation according to the defined parameters and scenarios. Ensure that the first principle-based simulation models replicate our plant's operations, including factors like equipment behavior, material flow, and work/energy dynamics. Evaluate the results and make sure that the simulation replicates real-world conditions as closely as possible.

validating and verifying simulation models is essential for their reliability and usefulness. Here are the key points: Validation: 1. Purpose: Assess model accuracy in representing the real system. 2. Methods: Expert judgment, sensitivity analysis, calibration, historical data comparison. 3. Importance: Valid models inspire confidence. Verification: 1. Purpose: Ensure error-free, correct model execution. 2. Methods: Code review, unit testing, integration testing. 3. Importance: Reliable simulations. 4. Balance: Strive for accuracy and correctness, iterate, and document assumptions. Remember, well-validated and verified models inform decision-making!

Reporting the process model is the essential step to highlight the insight and the trend. The report should explain well the detailed objective, procedure method, limitation, and result, pros and cons. We can use the chart, graphs, table or smart dashboarding to explain the result and make it reliable to implement

Analyze the simulation results using the predefined performance metrics. Common metrics include throughput, cycle time, resource utilization, and production costs. Compare these metrics to the baseline data to identify differences. - Sensitivity Analysis - What-if scenarios

- Conduct sensitivity analysis to understand how changes in various parameters or input variables affect the outcomes. - Performing what-if scenarios test different process modifications, equipment upgrades, or scheduling changes to assess their potential impact on operations.

Digital twin provide a rigours simulation tool using AI, adjusting simulation to give real outputs similar to actual plant outpiluts, then whenever you simulate changes results would be more accurate.