An intelligent sensing system for estimation of efficiency of carbon-capturing unit in a cement plant

Jadoon U. K., Ahmad I., Noor T., Kano M., ÇALIŞKAN H., Ahsan M.

Journal of Cleaner Production, vol.377, 2022 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 377
  • Publication Date: 2022
  • Doi Number: 10.1016/j.jclepro.2022.134359
  • Journal Name: Journal of Cleaner Production
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Business Source Elite, Business Source Premier, CAB Abstracts, Communication Abstracts, INSPEC, Metadex, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, Civil Engineering Abstracts
  • Keywords: Artificial intelligence, Greenhouse gases, Process flow sheeting, Soft sensors, Sustainability
  • Uşak University Affiliated: Yes

Abstract

In this study, an artificial intelligence-based framework was developed to monitor the efficiency of the carbon capture unit, i.e., the Sour Compression Unit (SCU) and a cryogenic unit, in a cement manufacturing plant. Initially, an Aspen Plus-based model of the SCU and the cryogenic unit was developed. The process model was then transformed into dynamic mode through the interfacing of MATLAB and Excel with Aspen Plus to capture real-time process behavior. Five hundred fifty data samples were generated by varying the process conditions, i.e., inlet water flow rate, temperature, pressure, etc. The dataset was then used to develop ensemble models. Output compositions of the models were CO2, SO2, and NO. The ensemble models were used as surrogates in Sobol and Fourier Amplitude Sensitivity Test (FAST) frameworks to find the most sensitive process conditions that affect the efficiency of the process. The correlation coefficients based on the ensemble models for estimation of the composition of CO2, SO2, and NO, were 0.9888, 0.9663, and 0.9970, respectively. Based on the sensitivity analysis, the inlet flue gas temperature and pressure were the dominant variables that affect the mole fraction of SO2 and NO in the flue gas. Besides, the CO2 recovery was found heavily dependent on temperature of the flash tanks. The proposed framework was highly accurate and will provide a base for real-time implementation of the concept of smart factory in cement manufacturing plants.