In the coal-based combustion and gasification processes, the mineral matter contained in the coal (predominantly oxides), is left as an incombustible residue, termed ash. Commonly, ash deposits are formed on the heat absorbing surfaces of the exposed equipment of the combustion/gasification processes. These deposits lead to the occurrence of slagging or fouling and, consequently, reduced process efficiency. The ash fusion temperatures (AFTs) signify the temperature range over which the ash deposits are formed on the heat absorbing surfaces of the process equipment. Thus, for designing and operating the coal-based processes, it is important to have mathematical models predicting accurately the four types of AFTs namely initial deformation temperature, softening temperature, hemispherical temperature, and flow temperature. Several linear/nonlinear models with varying prediction accuracies and complexities are available for the AFT prediction. Their principal drawback is their applicability to the coals originating from a limited number of geographical regions. Accordingly, this study presents computational intelligence (CI) based nonlinear models to predict the four AFTs using the oxide composition of the coal ash as the model input. The CI methods used in the modeling are genetic programming (GP), artificial neural networks, and support vector regression. The notable features of this study are that the models with a better AFT prediction and generalization performance, a wider application potential, and reduced complexity, have been developed. Among the CI-based models, GP and MLP based models have yielded overall improved performance in predicting all four AFTs. Int J Coal Sci Technol (2018) 5(4):486-507 https://doi.org/10.1007/s40789-018-0213-6Prediction of coal ash fusion temperatures using computational intelligence based models 487

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This chapter gives a thorough overview of image processing's uses and potential in industrial chemical engineering. Image processing can provide precise and in-depth information about chemical processes, products, and its significance in this field is highlighted. The foundations of image processing are covered in this chapter, including image formation and acquisition, image preprocessing, feature extraction, and selection. The applications of image-based process monitoring and control, image analysis for product quality control, and the newest developments and difficulties in machine learning in image-based chemical engineering are also covered. The section on machine learning in image-based chemical engineering gives a general overview of machine learning methods and how they are used in the field of chemical engineering. The chapter's discussion of image processing's limitations in chemical engineering, as well as current trends and future research prospects, come to close.