The full operation of an industrial processing facility with artificial intelligence has been the holy grail of Industry 4.0. One of the inherent difficulties is the enumerate and complex nature of processing information within an industrial plant. Hence, such data should be processed efficiently. This paper demonstrates the effectiveness of a deep auto-encoder neural network for the dimensionality reduction of industrial processing data. The deep auto-encoder neural network functions to intake all possible processing data from the processing system by sending it into an encoder neural network. Subsequently, the encoder condenses the data into highly compressed encoded variables. The network is trained in an unsupervised manner, where a decoder neural network simultaneously attempts to revert the encoded variables to their original form. Such a deep learning approach allows data to be highly compressed into lower dimensions. The coded variables retain critical information of the processing system, allowing reconstruction of the full process data. Auto-encoder neural networks are also able to provide noise removal for encoded data. For application, the encoded variable can be utilized as an effective dimension-reduced variable that can be used for plant-wide optimization. This paper also discusses the further applications of encoded variables for industrial process improvements using the Industrial Internet of Things (IIoT) technologies.