Optimizing Distillation Column Yield Using Historical Data

Aug 25, 2024, 8:15 AM

dist_tower_na.txt

Missing file

You have been assigned to a project where the goal is to maximize the yield of a distillation column. Process engineers from the site were able to stabilize the yield recently but the baseline is now lower than before.

As a data scientist, your task is to optimize the yield using historical data.

Data has been extracted and put in a csv

We will have a team meeting in about 30 min

The dataset has been successfully loaded. Here are the first few rows of the data:

Date	yield	OC1	Temp11	Temp12	PressureC1	TempC1	Temp1	FlowC1	Temp2	...	FlowC9	FlowC2	Temp9	Temp10	FlowC3	FlowC4	VapourPressure
2016-08-21T00:00:00	69.400623	3.1033	30.8983	489.99	215.0627	100.2204	139.9857	432.0636	377.8119	...	86.6133	69.9017	461.2371	513.9653	8.6279	10.5988	32.5026
2016-08-23T00:00:00	66.532666	4.7408	31.9099	480.2888	205.0999	100.2297	131.0470	487.4029	371.3060	...	97.6100	69.6900	450.3000	504.5145	8.7662	10.7560	NaN
2016-08-26T00:00:00	71.102193	4.2925	29.9165	486.6190	205.0304	100.3084	118.2666	437.3516	NaN	...	88.4967	70.5867	458.8101	508.9997	8.5319	10.5737	32.1666
2016-08-29T00:00:00	69.793481	4.4558	30.6229	491.1304	205.2561	95.5766	118.1769	481.8314	378.0028	...	95.3483	70.2167	462.5252	514.1794	8.6260	10.6695	30.4064
2016-08-30T00:00:00	71.489516	3.4600	29.4977	487.6475	205.0883	92.9052	120.7891	NaN	377.8871	...	82.9533	69.6567	NaN	511.0948	8.5939	NaN	30.9238

The dataset contains 25 columns, including the yield and various process parameters.

Next Steps:

Data Cleaning: Handle missing values and ensure data consistency.
Exploratory Data Analysis (EDA): Understand the relationships between yield and other variables.
Feature Engineering: Create new features if necessary.
Modeling: Build predictive models to optimize yield.

Would you like to proceed with data cleaning or start with exploratory data analysis (EDA)?

What are the key factors affecting the yield?

The correlation matrix heatmap has been displayed above. This heatmap shows the correlation between different variables in the dataset.

Key Factors Affecting Yield:

From the heatmap, we can observe the correlation values between the yield and other variables. The variables with higher absolute correlation values (positive or negative) are likely to have a significant impact on the yield.

Some key factors that might be affecting the yield include:

OC1: Shows a moderate positive correlation with yield.
Temp11: Shows a moderate negative correlation with yield.
PressureC1: Shows a moderate positive correlation with yield.
FlowC1: Shows a moderate positive correlation with yield.
TempC1: Shows a moderate negative correlation with yield.

These variables are good candidates for further analysis and modeling to optimize the yield.

Would you like to proceed with building a predictive model to optimize the yield or perform further exploratory data analysis?

Can you build a predictive model to optimize the yield?

The RandomForestRegressor model has been trained and evaluated. Here are the results:

Model Performance:

Mean Squared Error (MSE): 0.709
R-squared (R²): 0.349

Feature Importances:

Feature	Importance
Temp1	0.304
FlowC1	0.214
FlowC9	0.068
Temp4	0.046
TempC3	0.045
TempC1	0.035
PressureC1	0.034
Temp7	0.028
OC1	0.027
Temp2	0.026
FlowC2	0.025
Temp11	0.022
FlowC3	0.021
FlowC4	0.020
VapourPressure	0.020
Temp8	0.017
Temp9	0.011
Temp10	0.008
TempC2	0.007
Temp5	0.007
Temp3	0.007
Temp6	0.006
Temp12	0.004

The most important features affecting the yield are Temp1, FlowC1, and FlowC9.

Would you like to proceed with further optimization or any other analysis?

Can you visualize the feature importances?

Can you suggest ways to further optimize the yield?

To further optimize the yield, consider the following steps:

Feature Engineering:
- Create new features based on domain knowledge. For example, interaction terms between temperature and pressure might be significant.
- Use polynomial features to capture non-linear relationships.
Hyperparameter Tuning:
- Perform hyperparameter tuning on the RandomForestRegressor model using techniques like Grid Search or Random Search to find the best parameters.
Advanced Modeling Techniques:
- Experiment with other machine learning models such as Gradient Boosting Machines (GBM), XGBoost, or Neural Networks.
- Use ensemble methods to combine predictions from multiple models.
Cross-Validation:
- Use cross-validation techniques to ensure the model's robustness and avoid overfitting.
Data Augmentation:
- If possible, collect more data to improve the model's performance.
- Use synthetic data generation techniques to augment the dataset.
Domain-Specific Adjustments:
- Consult with domain experts to understand the underlying processes better and incorporate their insights into the model.
Regular Monitoring and Updating:
- Continuously monitor the model's performance and update it with new data to maintain its accuracy.

Would you like to proceed with any of these steps or have any specific area you want to focus on?

Can you try using a different machine learning model like XGBoost?

The XGBoost model has been trained and evaluated. Here are the results:

Model Performance:

Mean Squared Error (MSE): 0.881
R-squared (R²): 0.190

Feature Importances:

Feature	Importance
FlowC1	0.275
Temp1	0.181
TempC2	0.078
FlowC9	0.072
TempC3	0.070
PressureC1	0.048
TempC1	0.037
Temp2	0.032
VapourPressure	0.031
Temp9	0.025
Temp6	0.022
FlowC4	0.021
Temp4	0.017
Temp3	0.013
Temp11	0.013
OC1	0.012
Temp7	0.011
FlowC2	0.010
Temp12	0.010
FlowC3	0.009
Temp10	0.006
Temp8	0.005
Temp5	0.004

The most important features affecting the yield according to the XGBoost model are FlowC1, Temp1, and TempC2.

Would you like to visualize the feature importances or proceed with further optimization steps?

Can you visualize the predictions of the XGBoost model?

can you make a shap plot?

Can you explain the SHAP plot in more detail?

The SHAP summary plot provides a comprehensive view of the impact of each feature on the model's predictions. Here's a detailed explanation:

Feature Importance:
- The features are listed on the y-axis in descending order of importance. The most important feature is at the top.
SHAP Values:
- The x-axis represents the SHAP values, which indicate the impact of each feature on the model's output. A positive SHAP value means the feature increases the prediction, while a negative SHAP value means it decreases the prediction.
Color Coding:
- Each dot represents a single prediction (row) from the dataset. The color of the dot indicates the value of the feature from low (blue) to high (red).
Distribution of SHAP Values:
- The spread of the dots along the x-axis shows the distribution of the SHAP values for each feature. A wider spread indicates a higher impact on the model's predictions.
Interaction Effects:
- The plot can also reveal interaction effects between features. For example, if a feature has a high SHAP value only when another feature is at a certain level, this interaction can be observed in the plot.

In summary, the SHAP summary plot helps to understand which features are most influential in the model's predictions and how they affect the output. It provides both global and local interpretability of the model.

with the analysis done so far, please make an executive summary for my meeting with process engineers

Executive Summary: Yield Prediction Analysis for Distillation Tower

Overview

This analysis aims to predict the yield of a distillation tower using various process variables. We utilized an XGBoost regression model to understand the key factors influencing the yield and to make accurate predictions.

Data Summary

Dataset: The dataset contains various process variables recorded over time, including temperatures, pressures, and flow rates.
Total Features: 24 (excluding the date and yield columns)
Target Variable: Yield

Key Findings

Correlation Analysis

A correlation matrix was generated to understand the relationships between different process variables.
Key observations include strong correlations between certain temperature and flow variables, indicating potential areas for process optimization.

Model Performance

Model Used: XGBoost Regressor
Mean Squared Error (MSE): 0.881
R-squared (R²): 0.190

The model's performance indicates that while it captures some variability in the yield, there is room for improvement. The R² value suggests that approximately 19% of the variance in yield is explained by the model.

Feature Importance

The most important features affecting the yield according to the XGBoost model are:

FlowC1 (Importance: 0.275)
Temp1 (Importance: 0.181)
TempC2 (Importance: 0.078)
FlowC9 (Importance: 0.072)
TempC3 (Importance: 0.070)

SHAP Analysis

SHAP (SHapley Additive exPlanations) values were used to interpret the model's predictions.
The SHAP summary plot provides a detailed view of the impact of each feature on the model's output.
Key insights include the significant influence of FlowC1 and Temp1 on yield predictions, with FlowC1 having the highest impact.

Recommendations

Focus on Key Variables: Given the importance of FlowC1 and Temp1, process engineers should prioritize monitoring and optimizing these variables to improve yield.
Further Model Optimization: Consider additional feature engineering and model tuning to improve the predictive performance.
Regular Monitoring: Implement real-time monitoring of key variables to ensure they remain within optimal ranges.

Visualizations

Correlation Matrix: Provides an overview of relationships between process variables.
Feature Importances: Highlights the most influential features for yield prediction.
SHAP Summary Plot: Offers detailed interpretability of the model's predictions.

Conclusion

This analysis provides a foundational understanding of the factors influencing distillation tower yield. By focusing on key variables and further optimizing the model, process engineers can make data-driven decisions to enhance yield and overall process efficiency.

Would you like to explore any specific aspect further or need additional visualizations?

Get started with Vizly