Introduction to Artificial Intelligence and Machine Learning for Chemical Engineers
Expert-defined terms from the Professional Certificate in AI for Chemical Engineers course at London School of Planning and Management. Free to read, free to share, paired with a professional course.
Artificial Neural Network (ANN) – a computational model inspired by the s… #
Related terms: deep learning, backpropagation, activation function. Explanation: ANNs learn patterns by adjusting weights through training on data sets, enabling prediction, classification, or regression tasks. Example: Using an ANN to predict polymer viscosity from temperature and composition data. Practical application: Real‑time monitoring of reactor temperature profiles to optimize heat removal. Challenge: Overfitting when training data are limited, requiring regularization or cross‑validation techniques.
Bayesian Inference – a statistical method that updates the probability of… #
Related terms: prior distribution, posterior probability, Markov Chain Monte Carlo (MCMC). Explanation: By combining prior knowledge with new data, Bayesian inference provides a full probability distribution of model parameters rather than a single point estimate. Example: Estimating kinetic parameters of a catalytic reaction using experimental conversion data while incorporating literature values as priors. Practical application: Uncertainty quantification in process design, allowing risk‑aware decision making. Challenge: Computational expense of sampling high‑dimensional posterior spaces, often mitigated with advanced MCMC algorithms or variational methods.
Clustering – an unsupervised learning technique that groups data points b… #
Related terms: K‑means, hierarchical clustering, silhouette score. Explanation: Clustering partitions a data set into clusters such that intra‑cluster variance is minimized and inter‑cluster variance is maximized. Example: Grouping solvents by polarity, boiling point, and toxicity to aid solvent selection for extraction processes. Practical application: Identifying operating regimes in a distillation column where performance metrics behave similarly, facilitating simplified control strategies. Challenge: Determining the appropriate number of clusters and handling noisy or high‑dimensional data.
Decision Tree – a flowchart‑like model that splits data based on feature… #
Related terms: random forest, Gini impurity, pruning. Explanation: Each internal node represents a test on an attribute, each branch a outcome, and each leaf node a class or value prediction. Example: Predicting catalyst deactivation time based on feed composition, temperature, and pressure. Practical application: Rapid screening of design alternatives in a process synthesis study, where interpretability is valuable for engineers. Challenge: High variance and tendency to overfit, often addressed by ensemble methods like random forests or gradient boosting.
Ensemble Learning – a technique that combines multiple models to improve… #
Related terms: bagging, boosting, stacking. Explanation: By aggregating diverse learners, ensembles reduce variance, bias, or improve robustness compared to a single model. Example: Using a bagged ensemble of regression trees to predict product yield across different reactor configurations. Practical application: Fault detection in a plant where multiple sensor‑based classifiers vote on anomaly presence, increasing reliability. Challenge: Increased computational cost and reduced interpretability relative to single models.
Feature Engineering – the process of creating, selecting, and transformin… #
Related terms: dimensionality reduction, one‑hot encoding, principal component analysis (PCA). Explanation: Effective features capture underlying physical relationships, reduce noise, and enable algorithms to learn efficiently. Example: Converting raw temperature and pressure measurements into dimensionless groups such as Reynolds or Schmidt numbers for a fluid flow model. Practical application: Enhancing the accuracy of a machine‑learning model that predicts fouling rates in heat exchangers. Challenge: Requires domain expertise; automated methods may overlook subtle chemical engineering insights.
Gradient Descent – an optimization algorithm that iteratively updates mod… #
Related terms: learning rate, stochastic gradient descent (SGD), momentum. Explanation: At each step, the gradient of the loss function with respect to parameters is computed, and parameters are moved opposite to the gradient to minimize error. Example: Training a neural network to map feed composition to product purity by minimizing mean‑squared error. Practical application: Real‑time tuning of a soft sensor that estimates concentration in a reactor from temperature and pressure data. Challenge: Choosing appropriate learning rates and avoiding local minima or saddle points, especially in non‑convex loss landscapes.
Hyperparameter Tuning – the process of selecting optimal settings for mod… #
Related terms: grid search, random search, Bayesian optimization. Explanation: Hyperparameters such as tree depth, regularization strength, or network architecture influence model capacity and generalization. Example: Determining the optimal number of hidden layers and neurons for a deep network that predicts polymer molecular weight distribution. Practical application: Automating the selection of regularization parameters for a ridge regression model used in process optimization. Challenge: The search space can be large; exhaustive search is often infeasible, motivating efficient strategies like Bayesian optimization.
Kernel Methods – a class of algorithms that implicitly map data into high… #
Related terms: support vector machine (SVM), Gaussian kernel, kernel trick. Explanation: By computing inner products in the transformed space via kernels, complex nonlinear relationships can be captured without explicit feature construction. Example: Using an SVM with a radial basis function kernel to classify catalyst activity levels based on surface characterization data. Practical application: Classifying operating points of a reactor as stable or unstable from sensor signatures. Challenge: Selecting an appropriate kernel and tuning its parameters; computational cost grows with dataset size.
Logistic Regression – a statistical model used for binary classification… #
Related terms: odds ratio, sigmoid function, regularization. Explanation: The model applies the logistic (sigmoid) function to map linear predictor values to probabilities between 0 and 1. Example: Predicting whether a batch will meet product specification based on feed purity, temperature, and residence time. Practical application: Early‑stage alarm systems that flag potential off‑spec runs, allowing corrective action. Challenge: Limited to linear decision boundaries unless interaction terms or polynomial features are added.
Monte Carlo Simulation – a computational technique that uses random sampl… #
Related terms: variance reduction, Latin hypercube sampling, probabilistic risk assessment. Explanation: By generating many random realizations of uncertain inputs, the method quantifies output distributions and associated probabilities. Example: Simulating the impact of feed composition variability on product yield in a continuous reactor network. Practical application: Propagating measurement uncertainties through a process model to assess confidence intervals for key performance indicators. Challenge: Requires a large number of samples for accurate tail‑probability estimation; variance reduction methods can mitigate computational load.
Natural Language Processing (NLP) – a set of techniques for analyzing and… #
Related terms: tokenization, word embeddings, transformer models. Explanation: NLP enables extraction of information from unstructured text such as patents, research articles, or maintenance logs. Example: Automatically extracting reaction conditions and yields from scientific abstracts to populate a reaction database. Practical application: Building a searchable knowledge base for process engineers that surfaces relevant case studies based on query keywords. Challenge: Domain‑specific terminology and abbreviations in chemical engineering require customized vocabularies and training data.
Neural Architecture Search (NAS) – an automated method for discovering op… #
Related terms: search space, reinforcement learning, gradient‑based search. Explanation: NAS algorithms explore combinations of layers, connections, and hyperparameters to maximize performance on a validation set. Example: Designing a compact convolutional network to predict catalyst surface morphology from microscopy images. Practical application: Deploying efficient models on edge devices for on‑site quality control in a refinery. Challenge: Search process is computationally intensive; surrogate models or weight‑sharing techniques are often employed to reduce cost.
Overfitting – a modeling error where a learned model captures noise or ra… #
Related terms: regularization, cross‑validation, bias‑variance tradeoff. Explanation: An overfit model exhibits low training error but high error on unseen data because it has memorized specific training instances. Example: A deep network that perfectly predicts training set conversion data but fails on new feed compositions. Practical application: Implementing early stopping and dropout in training pipelines to maintain predictive accuracy for future batches. Challenge: Balancing model complexity with limited data, especially in early‑stage process development where experimental points are scarce.
Principal Component Analysis (PCA) – a linear dimensionality‑reduction te… #
Related terms: eigenvectors, loadings, variance capture. Explanation: By projecting data onto a lower‑dimensional subspace, PCA simplifies visualization and reduces computational load while retaining most information. Example: Reducing a set of 30 spectral features from infrared measurements to the first three principal components for downstream classification. Practical application: Accelerating the training of surrogate models for multivariate process simulations. Challenge: PCA assumes linear relationships; nonlinear manifolds may require kernel PCA or autoencoders.
Probabilistic Graphical Model – a framework that represents variables and… #
Related terms: Bayesian network, Markov random field, inference. Explanation: Nodes correspond to random variables, edges encode probabilistic relationships, allowing compact representation of joint distributions. Example: Modeling the interdependence of temperature, pressure, and catalyst activity in a reactor network using a Bayesian network. Practical application: Real‑time fault diagnosis by updating belief states as new sensor data arrive. Challenge: Learning accurate graph structures from limited data and performing efficient inference in large networks.
Random Forest – an ensemble of decision trees trained on bootstrapped sub… #
Related terms: bagging, feature importance, out‑of‑bag error. Explanation: Random forests reduce variance of individual trees while preserving interpretability through feature importance metrics. Example: Predicting product impurity levels from a mixture of process variables in a batch reactor. Practical application: Screening critical process parameters for sensitivity analysis in process intensification studies. Challenge: Large forests can become memory‑intensive; pruning or limiting tree depth may be necessary for deployment on embedded hardware.
Reinforcement Learning (RL) – a learning paradigm where an agent interact… #
Related terms: policy, Q‑learning, exploration‑exploitation. Explanation: The agent learns optimal control strategies by trial‑and‑error, receiving feedback in the form of rewards or penalties. Example: Training an RL controller to adjust feed flow rates in a continuous reactor to maintain target conversion while minimizing energy use. Practical application: Adaptive scheduling of batch operations in a plant to respond to fluctuating market demand. Challenge: Defining appropriate reward functions that capture multiple engineering objectives and ensuring safety during exploration phases.
Support Vector Machine (SVM) – a supervised learning algorithm that finds… #
Related terms: margin, kernel trick, soft margin. Explanation: By using kernel functions, SVMs can separate data that are not linearly separable in the original space. Example: Classifying catalyst samples as high‑ or low‑activity based on surface area, pore size distribution, and elemental composition. Practical application: Rapid screening of catalyst libraries to prioritize experimental testing. Challenge: Scaling to very large datasets; training time grows quadratically with sample size, often requiring approximation techniques.
Transfer Learning – a technique where a model trained on one task or data… #
Related terms: fine‑tuning, pre‑trained model, domain adaptation. Explanation: Knowledge captured in early layers of deep networks (e.g., feature detectors) can be reused, reducing the need for extensive training data. Example: Using a convolutional network pre‑trained on generic images to detect defects in polymer films after fine‑tuning on a small labeled set. Practical application: Accelerating development of visual inspection tools for quality control on production lines. Challenge: Mismatch between source and target domains can lead to negative transfer; careful selection of layers to freeze or adapt is required.
Uncertainty Quantification (UQ) – the process of characterizing and reduc… #
Related terms: propagation, sensitivity analysis, confidence interval. Explanation: UQ combines statistical methods, sampling techniques, and surrogate models to provide probabilistic bounds on outputs. Example: Propagating measurement errors in feed composition through a kinetic model to obtain a distribution of predicted conversion. Practical application: Designing safety margins in process control systems that account for sensor drift and model inaccuracies. Challenge: High‑dimensional uncertainty spaces demand efficient sampling; polynomial chaos expansions or Gaussian process surrogates can alleviate computational burden.
Variational Autoencoder (VAE) – a generative deep‑learning model that lea… #
Related terms: encoder, decoder, latent space. Explanation: VAEs optimize a loss function that balances reconstruction accuracy with latent space regularization, enabling smooth interpolation between data points. Example: Generating plausible molecular structures with desired physicochemical properties by sampling the latent space. Practical application: Rapid ideation of new solvent candidates for extraction processes, reducing reliance on trial‑and‑error experiments. Challenge: Ensuring generated samples respect chemical feasibility constraints; incorporation of domain‑specific rules or reinforcement signals may be necessary.
Wavelet Transform – a mathematical tool that decomposes a signal into loc… #
Related terms: continuous wavelet transform, discrete wavelet transform, time‑frequency analysis. Explanation: Unlike Fourier analysis, wavelets capture both frequency and temporal information, making them suitable for non‑stationary signals. Example: Denoising temperature sensor data from a reactor by removing high‑frequency noise while preserving transient spikes. Practical application: Fault detection in rotating equipment using vibration signals, where wavelet coefficients highlight abnormal patterns. Challenge: Selecting appropriate mother wavelet and decomposition level for a given process signal.
Zero‑Shot Learning – a paradigm where a model can recognize classes it ha… #
Related terms: semantic embedding, attribute space, generalized zero‑shot. Explanation: By mapping both seen and unseen classes into a shared representation, the model infers relationships and makes predictions for novel categories. Example: Classifying a newly developed catalyst type based on its reported elemental composition and surface descriptors without having explicit training examples. Practical application: Accelerating adoption of emerging technologies by providing preliminary performance estimates before experimental validation. Challenge: Quality of auxiliary information heavily influences accuracy; sparse or noisy attribute data can degrade performance.
Gradient Boosting Machine (GBM) – an ensemble method that builds decision… #
Related terms: XGBoost, learning rate, regularization. Explanation: By focusing on residuals, GBMs achieve high predictive accuracy and can handle various loss functions for regression or classification. Example: Predicting energy consumption of a distillation column based on feed composition, reflux ratio, and operating pressure. Practical application: Optimization of plant-wide energy integration where accurate surrogate models guide pinch analysis. Challenge: Sensitive to hyperparameter settings; over‑fitting can occur if trees are too deep or learning rate is too high, necessitating careful tuning and early stopping.
Hybrid Modeling – an approach that combines first‑principles (mechanistic… #
Related terms: gray box model, physics‑informed neural network, model fusion. Explanation: The mechanistic part provides structure and interpretability, while the data‑driven part compensates for unknown phenomena or parameter uncertainties. Example: Embedding a mass‑balance model of a reactor within a neural network that learns unmodeled heat transfer resistances. Practical application: Real‑time soft sensors that estimate unmeasured states (e.g., catalyst activity) using readily available measurements and a mechanistic backbone. Challenge: Balancing the contribution of each component to avoid dominance of noisy data or overly rigid physics constraints.
Explainable AI (XAI) – a set of techniques that make the decisions of com… #
Related terms: SHAP values, LIME, model interpretability. Explanation: XAI methods attribute importance to input features, generate visual explanations, or simplify models while preserving performance. Example: Using SHAP to reveal that temperature and catalyst loading are the most influential factors in a neural network predicting polymer molecular weight distribution. Practical application: Gaining regulatory acceptance for AI‑driven control strategies by demonstrating compliance with safety standards. Challenge: Trade‑off between explanation fidelity and computational cost; highly accurate models may be harder to interpret without approximations.
Active Learning – a strategy where the learning algorithm selectively que… #
Related terms: query strategy, uncertainty sampling, budgeted learning. Explanation: By focusing on ambiguous or boundary cases, the model improves performance efficiently. Example: Iteratively selecting experimental conditions that maximize expected reduction in prediction error for a kinetic model. Practical application: Accelerating catalyst screening campaigns where each experiment is costly, by guiding the lab toward the most promising compositions. Challenge: Designing reliable acquisition functions that balance exploration of unknown regions with exploitation of known high‑performing areas.
Convolutional Neural Network (CNN) – a deep learning architecture special… #
Related terms: kernel, pooling, feature map. Explanation: Convolutions apply learnable filters that detect local patterns, enabling the network to recognize complex visual structures. Example: Analyzing microscopy images of polymer morphology to predict mechanical properties. Practical application: Automated visual inspection of product surfaces on a production line, detecting defects with high speed and accuracy. Challenge: Requires large labeled image datasets; transfer learning and data augmentation are common remedies in chemical engineering contexts.
Recurrent Neural Network (RNN) – a class of neural networks designed for… #
Related terms: LSTM, GRU, time‑series forecasting. Explanation: RNNs capture temporal dependencies, making them suitable for modeling dynamic processes. Example: Predicting future temperature profiles in a batch reactor based on past sensor readings. Practical application: Forecasting demand for a chemical product to inform inventory and production planning. Challenge: Vanishing or exploding gradients hinder learning long‑range dependencies; gated architectures like LSTM mitigate this issue.
Gaussian Process (GP) – a non‑parametric Bayesian model that defines a di… #
Related terms: kernel function, covariance matrix, kriging. Explanation: GPs interpolate training data points while quantifying confidence, making them valuable for surrogate modeling. Example: Building a GP surrogate for a computationally expensive CFD simulation of reactor flow fields. Practical application: Bayesian optimization of operating conditions where the GP guides the search toward regions of high expected improvement. Challenge: Computational complexity scales cubically with the number of training points; sparse approximations or inducing point methods are used for larger datasets.
Data Augmentation – techniques that artificially increase the size and di… #
Related terms: synthetic data, noise injection, geometric transformations. Explanation: Augmentation helps prevent overfitting, especially when original data are scarce. Example: Rotating, flipping, and adding Gaussian noise to images of catalyst particles to expand the training set for a CNN. Practical application: Enhancing robustness of a fault‑detection model for sensor drift by simulating varied sensor offsets. Challenge: Transformations must be physically plausible; unrealistic augmentations can mislead the learning algorithm.
Feature Selection – the process of identifying a subset of relevant varia… #
Related terms: recursive elimination, mutual information, lasso. Explanation: Reducing dimensionality improves interpretability, reduces overfitting, and lowers computational cost. Example: Selecting temperature, pressure, and catalyst concentration as key predictors for reaction rate among a larger set of measured variables. Practical application: Streamlining sensor deployment by focusing on the most informative measurements for a soft sensor network. Challenge: Inter‑feature correlations can mask importance; wrapper methods that evaluate subsets in the context of a specific model often yield better results than filter methods alone.
Hyperdimensional Computing – an emerging paradigm that represents data as… #
Related terms: binary spatter code, vector symbolic architecture, bundling. Explanation: Hypervectors can encode complex relationships compactly and are robust to noise, enabling efficient implementation on neuromorphic hardware. Example: Encoding process states (e.g., inlet composition, temperature, flow rate) into a hypervector for rapid similarity search in a large historical database. Practical application: Real‑time anomaly detection on edge devices with limited power consumption. Challenge: Developing domain‑specific encoding schemes that preserve chemical meaning while leveraging the mathematical properties of hyperdimensional spaces.