This paper aims to develop a CO2 emission model of an acid gas incinerator using Nelder-Mead least squares support vector regression (LS-SVR). The Malaysia Department of Environment is actively imposing the Clean Air Regulation to mandate heavy industries to comply with emission limits. One of the latest measures is to mandate the installation of analytical instrumentation known as a continuous emission monitoring system (CEMS) to report the emission level online to the Department of Environment office. As a hardware-based analyser, CEMS is expensive, maintenance intensive and often unreliable. Therefore, software predictive techniques are often preferred and considered a feasible alternative to replace the CEMS for regulatory compliance. The LS-SVR model is built based on the emissions from an acid gas incinerator that operates in a liquefied natural gas complex. Simulated annealing is first used to determine the initial hyper-parameters, which are further optimized based on the performance of the model using a Nelder-Mead simplex algorithm. The LS-SVR model is shown to outperform a benchmark model based on back-propagation neural networks in both training and testing data. © The Author(s) 2012.