Cement production is one of the most energy-intensive manufacturing industries, and the milling circuit of cement plants consumes around 4% of a year's global electrical energy production. It is well understood that modeling and digitalizing industrial-scale processes would help control production circuits better, improve efficiency, enhance personal training systems, and decrease plants' energy consumption. This tactical approach could be integrated using conscious lab (CL) as an innovative concept in the internet age. Surprisingly, no CL has been reported for the milling circuit of a cement plant. A robust CL interconnect datasets originated from monitoring operational variables in the plants and translating them to human basis information using explainable artificial intelligence (EAI) models. By initiating a CL for an industrial cement vertical roller mill (VRM), this study conducted a novel strategy to explore relationships between VRM monitored operational variables and their representative energy consumption factors (output temperature and motor power). Using SHapley Additive exPlanations (SHAP) as one of the most recent EAI models accurately helped fill the lack of information about correlations within VRM variables. SHAP analyses highlighted that working pressure and input gas rate with positive relationships are the key factors influencing energy consumption. eXtreme Gradient Boosting (XGBoost) as a powerful predictive tool could accurately model energy representative factors by R-square ever 0.80 in the testing phase. Comparison assessments indicated that SHAP-XGBoost could provide higher accuracy for VRM-CL structure than conventional modeling tools (Pearson correlation, Random Forest, and Support vector regression.
A series of Poly(amide-imide)/montmorillonite nanocomposites containing N -pyromellitimido-L-phenyl alanine moiety in the main chain were synthesized by a convenient solution intercalation technique. Poly(amideimide) (PAI) 5 as a source of polymer matrix was synthesized by the direct polycondensation reaction of Npyromellitimido-L-phenyl alanine 3 with 4,4'-diamino diphenyl ether 4 in the presence of triphenyl phosphite (TPP), CaCl 2 , pyridine and N -methyl-2-pyrrolidone (NMP). The resulting nanocomposite films were characterized by Fourier transform infrared spectra (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). The results showed that organo-modified clay was dispersed homogeneously in PAI matrix. TGA indicated an enhancement of thermal stability of new nanocomposites compared with the pure polymer.
A series of Poly(amide-imide)/montmorillonite nanocomposites containing N -pyromellitimido-L-phenyl alanine moiety in the main chain were synthesized by a convenient solution intercalation technique. Poly(amideimide) (PAI) 5 as a source of polymer matrix was synthesized by the direct polycondensation reaction of N -pyromellitimido-Lphenyl alanine 3 with 4,4'-diamino diphenyl ether 4 in the presence of triphenyl phosphite (TPP), CaCl2, pyridine and N -methyl-2-pyrrolidone (NMP). The resulting nanocomposite films were characterized by Fourier transform infrared spectra (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and thermogravimetric analysis (TGA). The results showed that organo-modified clay was dispersed homogeneously in PAI matrix. TGA indicated an enhancement of thermal stability of new nanocomposites compared with the pure polymer.Keywords: Poly(amide-imide); nanocomposite; thermal properties; ether moieties Users without a subscription are not able to see the full content. Please, subscribe or login to access all content.
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