Telemetry Data Compression Algorithm Using Balanced Recurrent Neural Network and Deep Learning

Telemetric information is great in size, requiring extra room and transmission time. There is a significant obstruction of storing or sending telemetric information. Lossless data compression (LDC) algorithms have evolved to process telemetric data effectively and efficiently with a high compression ratio and a short processing time. Telemetric information can be packed to control the extra room and association data transmission. In spite of the fact that different examinations on the pressure of telemetric information have been conducted, the idea of telemetric information makes pressure incredibly troublesome. The purpose of this study is to offer a subsampled and balanced recurrent neural lossless data compression (SB-RNLDC) approach for increasing the compression rate while decreasing the compression time. This is accomplished through the development of two models: one for subsampled averaged telemetry data preprocessing and another for BRN-LDC. Subsampling and averaging are conducted at the preprocessing stage using an adjustable sampling factor. A balanced compression interval (BCI) is used to encode the data depending on the probability measurement during the LDC stage. The aim of this research work is to compare differential compression techniques directly. The final output demonstrates that the balancing-based LDC can reduce compression time and finally improve dependability. The final experimental results show that the model proposed can enhance the computing capabilities in data compression compared to the existing methodologies.

Reducing total annual cost and CO2 emissions in batch distillation for separating ternary wide boiling mixtures using vapor recompression heat pump

This paper presents cost-effective heat pump assisted vapor recompression (VRC) design algorithms for the separation of ternary wide boiling mixture in batch distillation in order to reduce total annual cost (TAC) and carbon dioxide (CO2) emissions. A minimum TAC and CO2 is required by the batch distillation process industry for any investments in heat integrated systems, such as VRC. Consequently, the design conditions for implementing VRC should be chosen such that the energetic performance is maximum at minimum TAC. The model system selected in this paper is an application involving high temperature lift, that is, hexanol–octanol–decanol ternary wide boiling mixture. First, a systematic simulation algorithm was developed for conventional multicomponent batch distillation (CMBD) and single-stage vapor recompressed multicomponent batch distillation (SiVRMBD) to determine the optimal number of stages based on the maximum TAC savings. The SiVRMBD saves more energy and TAC than CMBD. However, SiVRMBD has a high compression ratio (CR) throughout the operation, which is not practically feasible for the batch distillation processing. Second, in order to increase the performance and minimize the SiVRMBD weakness, a novel optimal multi-stage vapor recompression algorithm was proposed to operate at the lowest possible CR (<3.5) throughout the batch operation while also conserving the most TAC. Overall, the findings suggest that the proposed optimal multi-stage VRC reduces TAC and CO2 emissions significantly when compared to CMBD. Finally, the influence of the different feed compositions on VRC performance is also studied.