Working with data in chemical engineering isn't what most people think
The industry runs on physical chemistry, transport phenomena, and reaction kinetics. Data science didn't replace any of that. It sits on top of it and deals with the mess that comes when you have a process running for twelve years and no one can tell you which sensor is drifting. I've spent most of my career bridging that gap between the DCS logs and whatever model someone decided to deploy on the floor. Start with your data source. In practice, that's usually a historian — OSIsoft PI, AspenTech IP.21, or sometimes a poorly documented SQL dump from 2008. The first step isn't modeling. It's proving your data exists and is trustworthy. I once spent three weeks trying to build a soft sensor for a distillation column, only to realize the temperature transmitter on tray 12 was wired to the wrong channel on the analog input card. The model gave me R-squared of 0.94. Completely useless because the input was garbage. You spend about forty percent of your time just cleaning what should have been clean in the first place. Feature selection matters more than algorithm choice. This is the part most people get backwards. They throw a random forest or gradient boosting at the problem and call it done. The real work is figuring out which variables actually move the target. In a reactor temperature prediction task, I found that the feed composition flow rates were more predictive than the actual measured reactor temperature history. Counter-intuitive, sure, but it makes sense if you think about it — the composition sets the reaction rate, the temperature is just the lagging response. If you include the current temperature in your feature set, you've leaked information and your model will look great offline and fail the moment you try to run it in real time.
Time alignment is another thing that nobody warns you about. Sensors sample at different rates. The DCS might be logging every five seconds, the lab analyzer every thirty minutes, and the batch record spreadsheet gets updated by shift supervisors at their discretion. When you merge these, you need to interpolate carefully. Linear interpolation is usually fine for smooth process variables. For lab data, don't interpolate at all — treat it as a separate check against your predictions rather than a training signal. I learned this the hard way on a catalyst activity estimation project where interpolating weekly lab results into daily predictions made the model chase noise instead of learning the actual decay curve. For actual model building, start simple. A linear regression or partial least squares model on properly aligned data will outperform a neural network in most process scenarios. Neural networks need orders of magnitude more data than you have. If your dataset covers six months of normal operation with maybe two upsets, you don't have enough data for deep learning. You have enough for a well-regularized PLS model and a proper understanding of your residuals. Validation is where most projects fall apart. Don't do random train-test splits on time-series process data. Split by time. Train on months one through eight, test on months nine and ten. If you randomly shuffle, your validation score will be inflated because the model is effectively seeing tomorrow's data during training. This is the single most common mistake I see when reviewing other people's work. Model performance drops by twenty to thirty percent when validated correctly against temporal splits compared to random splits.
When deployment actually matters — and it usually doesn't in my experience, which is its own problem — keep the model architecture simple enough that a process engineer can debug it without needing a PhD in machine learning. Export your features, predictions, and residuals to a database they can query. Add a dashboard that shows predicted versus actual with confidence bands. That's it. The model is going to fail anyway when conditions drift outside your training envelope, so make the failure visible instead of hidden. There are tools that help. Python with pandas and scikit-learn is the standard for a reason. MATLAB still has a place if your team is already invested in it. For anything involving differential equations or first-principles constraints, consider hybrid approaches where you embed physics into the loss function instead of training purely from data. This tends to generalize better outside your training range, which is exactly when pure data models fail hardest.
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What this approach won't fix
Data Science In Chemical Engineering doesn't solve bad instrumentation. If your pressure transducers are calibrated annually and your last calibration report shows fifteen percent drift, no amount of machine learning will recover accuracy. Fix the measurement layer first. It also doesn't compensate for poor process understanding. I've seen projects where the entire dataset was derived from a period of abnormal operation — a startup transient or a catalyst swap. The model learned the wrong thing and performed flawlessly on the wrong metric. Always, always check whether your training data actually represents normal steady-state operation before you invest any modeling effort. The biggest bottleneck in most organizations isn't the algorithm. It's access to historical data, domain knowledge from engineers who will actually explain what happened during that upset in November 2019, and patience to iterate through feature engineering rather than skipping straight to model training. All three take time. The return on investment, when it comes, is usually in early fault detection rather than real-time optimization. You'll catch a heat exchanger fouling twenty hours before it trips rather than fifteen minutes, and that's genuinely useful. Anything beyond that is usually marketing.
