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The quality of coalespecially its high ash contentsignificantly affects the performance of coal-based processes.Coal gasification is a cleaner and an efficient alternative to the coal combustion for producing the syngas. The high-ash coals are found in a number of countries, and they form an important source for the gasification. Accordingly, in this study, extensive gasification experiments were conducted in a pilot-plant scale fluidized-bed coal gasifier (FBCG) using high-ash coals from India.Specifically, the effects of eight coal and gasifier process related parameters on the four gasification performance variables, namely CO+H 2 generation rate, syngas production rate, carbon conversion, and heating value of the syngas, were rigorously studied. The data collected from these experiments were used in the FBCG modeling, which was conducted by utilizing two artificial intelligence (AI) strategies namely genetic programming (GP) and artificial neural networks (ANNs). The novelty of the GP formalism is that it searches and optimizes both the form and parameters of an appropriate linear/nonlinear function that best fits the given process data. The original eight-dimensional input space of the FBCG models was reduced to three-dimensional space using the principal component analysis (PCA) and the PCA-transformed three variables were used in the AI-based FBCG modeling. A comparison of the GP and ANN-based models reveals that their output prediction accuracies and the generalization performance vary from good to excellent as indicated by the high training and test set correlation coefficient magnitudes lying between 0.92 and 0.996. This study also presents results of the sensitivity analysis performed to identify those coal and process related parameters, which significantly affect the FBCG process performance. 49 These measures are expected to result in a high coal conversion 50 efficiency and lower environmental impact. 1 The gasification of 51 coal is such a promising clean coal technology. 2 The typical 52 thermal efficiencies of the conventional pulverized-fuel (PF)-53 fired power stations are approaching 37%, whereas supercritical 54 PF units can achieve net efficiencies of 47%. 3 In comparison, 55 power generation using an integrated gasification combined-56 cycle (IGCC) system has achieved thermal efficiencies of 57 approximately 47%, 4 and it is believed that the efficiencies 58 exceeding 50% are possible in the near future. 3,5 The newer gas 59 turbine concepts and increased process temperatures are 60 targeting efficiencies up to 65%. 5 61 The gasification technology, being environment-friendly, is a 62 potential alternative to the conventional coal combustion-based 63 power generation. The conventional thermal power plants with 64 steam cycles alone cannot achieve the high efficiency targets, 65 and hydrogen production from the combustion plants is not 66 feasible. These limitations are not applicable to the gasification 67 technologies and they possess several other advantages as well

The principal deficiency of the widely utilized Alfrey-Price (AP) scheme for computing reactivity ratios in the widely used free radical copolymerization is that it ignores important factors, such as the steric effects. This often leads to inaccurate reactivity ratio predictions by AP model. Accordingly, in this study, exclusively data-driven, Q-e parameter-based new models have been developed for the reactivity ratio prediction in free radical copolymerization. In the model development, a novel artificial intelligence formalism known as "genetic programming (GP)" that performs symbolic regression has been employed. The GPbased models possess a different functional form than AP model. Further, parameters of GP-based models were finetuned using Levenberg-Marquardt (LM) nonlinear regression method. A comparison of AP, GP and GP-LM as well as artificial neural network (ANN)-based models indicates that GP and GP-LM models exhibit superior reactivity ratio prediction accuracy and generalization performance (with correlation coefficient magnitudes close to or greater than 0.9) when compared with AP and ANN models. The GPbased reactivity ratio prediction models developed here due to their higher accuracy and generalization capability have the potential of replacing the widely used AP models.