Sang Jin Park

As the technology is emerging, the process industries are actively migrating to Industry 4.0 to optimize energy, production, profit, and the quality of products. It should be noted that real-time process monitoring is the area where most of the energies are being placed for the sake of optimization and safety. Big data and knowledge-based platforms are receiving much attention to provide a comprehensive decision support system. In this study, the Advanced Advisory system for Anomalies (AAA) is developed to predict and detect the abnormal operation in fired heaters for real-time process safety and optimization in a petrochemical plant. This system predicts and raises an alarm for future problems and detects and diagnoses abnormal conditions using root cause analysis (RCA), using the combination of FMEA (failure mode and effects analysis) and FTA (fault tree analysis) techniques. The developed AAA system has been integrated with databases in a petrochemical plant, and the results have been validated well by testing the application over an extensive period. This AAA online system provides a flexible architecture, and it can also be integrated into other systems or databases available at different levels in a plant. This automated AAA platform continuously monitors the operation, checks the dynamic conditions configured in it, and raises an alarm if the statistics exceed their control thresholds. Moreover, the effect of heaters’ abnormal conditions on efficiency and other KPIs (key performance indicators) is studied to explore the scope of improvement in heaters’ operation.

Biomass gasification is the most reliable thermochemical conversion technology for the conversion of biomass into gaseous fuels such as H₂, CO, and CH₄. The performance of a gasification process can be estimated using thermodynamic equilibrium models. This type of model generally assumes the system reaches equilibrium, while in reality the system may only approach equilibrium leading to some errors between experimental and model results. In this study non-stoichiometric equilibrium models are modified and improved with correction factors inserted into the design equations so that when the Gibbs free energy is minimized model predictions will more closely match experimental values. The equilibrium models are implemented in MatLab and optimized based on experimental values from the literature using the optimization toolbox. The modified non-stoichiometric models are shown to be more accurate than unmodified models based on the calculated root mean square error values. These models can be applied for various types of solid biomass for the production of syngas through biomass gasification processes such as wood, agricultural, and crop residues.

To help meet the global demand for energy and reduce the use of fossil fuels, alternatives such as the production of syngas from renewable biomass can be considered. This conversion of biomass to syngas is possible through a thermochemical gasification process. To design such gasification systems, model equations can be formulated and solved to predict the quantity and quality of the syngas produced with different operating conditions (temperature, the flow rate of an oxidizing agent, etc.) and with different types of biomass (wood, grass, seeds, food waste, etc.). For the comparison of multiple different types of biomass and optimization to find optimal conditions, simpler models are preferred which can be solved very quickly using modern desktop computers. In this study, a number of different stoichiometric thermodynamic models are compared to determine which are the most appropriate. To correct some of the errors associated with thermodynamic models, correction factors are utilized to modify the equilibrium constants of the methanation and water gas shift reactions, which allows them to better predict the real output composition of the gasification reactors. A number of different models can be obtained using different correction factors, model parameters, and assumptions, and these models are compared and validated against experimental data and modelling studies from the literature.

This work clearly shows that Aloe arborescens but not gels from Aloe vera, a common juice‐type product of Aloe, exerted anti‐skin wrinkling effects, and these effects were greatly enhanced by lactic acid fermentation with Lactobacillus plantarum. Treatment with the extract from the fermentation process (FE) at a dose of 0.5% highly activated human fibroblast cells by up to 175%, whereas 140% activation and 105% activation were observed with the extract obtained using conventional water extraction (WE) and the gel from A. vera (GE), respectively. The treatment of human fibroblasts with FE at a dose of 0.5% increased collagen production by up to 170% and inhibited MMP‐1 synthesis to 48%, which is likely due to its high antioxidant activity because the WE and GE showed markedly lower effects compared with those of the FE. Interestingly, the FE exhibited a profile dominated by relatively low‐molecular‐weight (MW) polysaccharides: 20% of the total polysaccharides in the FE were in the MW weight range of 600 to 900, whereas 95% of the total polysaccharides in the GE were in the MW range of 200,000 to 300,000. This result suggests that the larger polysaccharide molecules in the extract might be broken down during lactic acid fermentation, and the easy penetration of the small molecules in the extract into fibroblast cells thus results in improved anti‐skin wrinkling effects. This conclusion is also supported by the finding that the FE and WE, but not the GE, contained similar amounts of barbaloin, a strong antioxidant eluted from A. arborescens through the fermentation process. Therefore, this study strongly indicates that the enhanced anti‐skin wrinkling effects of the FE are most likely due to synergistic effects between the barbaloin and the low‐MW polysaccharides retained after the fermentation process.

In this study, optimizations on separation of Toluene and 1-Butanol mixture is carried out by pressure swing distillation process. New heat integration methods are applied and compared to conventional PSD process by using vapor recompression technique combined with pressure swing distillation process. New heat integration methods show positive results to decrease the utilities used in the process. A simulator Aspen HYSYS V8.4 is used for simulating all processes. Cost analysis is also performed and Aspen HYSYS V8.4 optimizer program is used for optimizing profit for the process. Pressure is also optimized for minimum heat duty requirements in the process and the ultimate concern is to minimize the total reboiler heat duties and overall operating cost for the system which results into increasing profit.