The ALAMO approach to machine learning

Abstract

ALAMO is a computational methodology for leaning algebraic functions from data. Given a data set, the approach begins by building a low-complexity, linear model composed of explicit non-linear transformations of the independent variables. Linear combinations of these non-linear transformations allow a linear model to better approximate complex behavior observed in real processes. The model is refined, as additional data are obtained in an adaptive fashion through error maximization sampling using derivative-free optimization. Models built using ALAMO can enforce constraints on the response variables to incorporate first-principles knowledge. The ability of ALAMO to generate simple and accurate models for a number of reaction problems is demonstrated. The error maximization sampling is compared with Latin hypercube designs to demonstrate its sampling efficiency. ALAMO's constrained regression methodology is used to further refine concentration models, resulting in models that perform better on validation data and satisfy upper and lower bounds placed on model outputs.

Hybrid Semi‐parametric Modeling in Separation Processes: A Review

“…ALAMO was recently extended to restrict the output space of the surrogate by allowing the implementation of physical knowledge in the form of constraints . The performance of this framework is shown in several chemical engineering applications in , . Algorithms for global optimization of constrained gray‐box computational problems (ARGONAUT) follow similar ideas in the context of global optimization of combined data‐driven and first‐principle models .…”

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

“…Highly popular choices for surrogate models there are Gaussian process regression models [108] and artificial neural networks [109]. More recently, methods for adaptive sampling and training of surrogate models have been applied as in ALAMO [110] and the combination of surrogate models with detailed model parts to form graybox or hybrid models [111]. On the dynamic side, the whole topic of surrogate modeling and reduced-order modeling can also be approached through the lens of system identification.…”

A Machine Learning Approach to Correlation Development Applied to Fin-Tube Bundle Heat Exchangers

“…The model is subsequently tested, exploited, and improved through the use of ive-free optimization solvers that adaptively sample new simulation or experimental points. re information about ALAMO, see Cozad et al [16,19] and Wilson and Sahinidis [20]. e functional form of a regression model is assumed to be unknown to ALAMO.…”