Manufacturing polypropylene (PP) composites to meet customers’ needs is difficult, time-consuming, and costly, owing to the ever-increasing diversity and complexity of the corresponding specifications and the trial-and-error method currently used to satisfy the required physical properties. To address this issue, we developed three models for predicting the physical properties of PP composites using three machine learning (ML) methods: multiple linear regression (MLR), deep neural network (DNN), and random forest (RF). Further, the industrial data of 811 recipes were acquired to verify the developed models. Data categorization was performed to account for the differences between data and the fact that different recipes require different materials. The three models were then deployed to predict the flexural strength (FS), melting index (MI), and tensile strength (TS) of the PP composites in nine case studies. The predictive performance results differed according to the physical properties of the composites. The FS and MI prediction models with MLR exhibited the highest R² values of 0.9291 and 0.9406. The TS model with DNN exhibited the highest R² value of 0.9587. The proposed models and study findings are useful for predicting the physical properties of PP composites for recipes and the development of new recipes with specific physical properties.

In the pulp and paper industry, black liquor, which is a biomass resource, is burned to produce electricity. Black liquor is concentrated to 21 wt% water through an evaporator before being burned in a boiler. For the evaporator, a multiple-effect evaporator (MEE) is mainly used, but it requires a large amount of energy and cost. Therefore, it is crucial to reduce energy and cost of evaporation process. Hence, this study suggested a novel process model that integrated mechanical vapor recompression (MVR) with MEE to increase the energy efficiency. The suggested MVR-assisted evaporation process was composed of preheating and evaporation processes to effectively concentrate black liquor. In addition, it reduced the steam consumption of MEE by using MVR, which uses relatively inexpensive electric energy in the pre-evaporation process. In the simulation results, the steam, electricity consumption, and the latent heat recovered from the secondary vapor of the suggested process were quantitatively analyzed to verify the energy efficiency. The results indicate that the proposed process increases substantial energy efficiency compared to the conventional process. Then, the appropriateness of the suggested process was evaluated by the techno-economic analysis. The total annualized cost (TAC) is determined for both current and potential future economic benefits. TAC of the MVR-assisted MEE configuration can be reduced by up to 77.54%.

Reactive dyeing is primarily used in the textile industry to achieve a high level of productivity for high-quality products. This method requires heating a large amount of freshwater for dyeing and cooling for the biological treatment of discharged wastewater. If the heat of the wastewater discharged from the textile industry is recovered, energy used for heating freshwater and cooling wastewater can be significantly reduced. However, the energy efficiency of this industry remains low, owing to the limited use of waste heat. Hence, this study suggested a cost-optimal heat exchanger network (HEN) in a heat pump-assisted textile industry wastewater heat recovery system with maximizing energy efficiency simultaneously. A novel two-step approach was suggested to develop the optimal HEN in heat pump-assisted textile industry wastewater heat recovery system. In the first step, the system was designed to integrate the heat exchanger and heat pump to recover waste heat effectively. In the second step, the HEN in the newly developed system was retrofitted using super-targeted pinch analysis to minimize cost and maximize energy efficiency simultaneously. As a result, the proposed wastewater heat recovery system reduced the total annualized cost by up to 43.07% as compared to the conventional textile industry lacking a wastewater heat recovery system. These findings may facilitate economic and environmental improvements in the textile industry.

At public bus stops, NO_X pollutants discharged by regularly stopping buses quickly accumulate, exposing waiting passengers to high levels of air pollutants, which creates a threat to public health. The environmental protection agency (EPA) presents air quality standards for NO_X, a significant pollutant that causes lung diseases such as asthma when exposed to the human body. To handle this problem, air purification systems are installed inside bus stops in many public places. However, it is challenging to maintain a low concentration of NO_X inside public bus stops due to the persistent inflow of bus exhaust gas. Therefore, it is crucial to design an optimal location for an air purification system to meet air environment standards for respiratory areas. This study proposed a computational fluid dynamics (CFD)-based optimal installation strategy for an air purification system to minimize NO_X exposure inside a public bus stop. The CFD model was developed to numerically analyze NO₂ exposure with the actual design value for a public bus stop in Ulsan, South Korea. The local NO₂ concentration was evaluated in the human breathing zone. The case study was performed according to the locations of the inlet and outlet of the air purification system. A transient CFD simulation was performed to analyze the effect of the air purification system on pollutants generated from the stationary bus by time flow in various cases. NO₂ concentration and exposure reduction effectiveness (ERE) were analyzed and compared for each case in the breathing zone. In the optimal case, the ERE of NO₂ was confirmed to be 35.9 %, and the NO₂ concentration according to the air quality standards of EPA could be maintained at 0.1 ppm or less. The theoretical framework proposed in this study can be generalized to design air purification systems for general external facilities.

Steam traps in large facilities need continuous maintenance to prevent corrosion and other damage that could pose a considerable threat to a facility and its workers. However, a significant amount of human resources is required for the maintenance of steam traps. An automatic method to inform stakeholders regarding maintenance cycles will be beneficial for the maintenance process. Therefore, an optimal maintenance priority decision model is developed in this study to establish an efficient steam trap management system. First, the frequency of failures, installation locations, and specifications of steam traps were determined as parameters causing a failure. A relative score and conversion score are calculated for each parameter. The final conversion score is the sum of the conversion score multiplied by the corresponding steam trap data weight factor. Steam traps within the range requiring inspection are classified as high priority cases. Experimental results confirmed that the failure accuracy rate is approximately 95%, and the average failure error rate is within 3%. Additionally, the number of steam traps to be checked was reduced by 3616. The proposed model significantly reduces maintenance in commercial industries.

Explosives, especially those used for military weapons, have a short lifespan and their performance noticeably deteriorates over time. These old explosives need to be disposed of safely. Fluidized bed incinerators (FBIs) are safe for disposal of explosive waste (such as TNT) and produce fewer gas emissions compared to conventional methods, such as the rotary kiln. However, previous studies on this FBI process have only focused on minimizing the amount of NOx emissions without considering the operating and unitality costs (i.e., total cost) associated with the process. It is important to note that, in general, a number of different operating conditions are available to achieve a target NOx emission concentration and, thus, it requires a significant computational requirement to compare the total costs among those candidate operating conditions using a computational fluid dynamics simulation. To this end, a novel framework is proposed to quickly determine the most economically viable FBI process operating condition for a target NOx concentration. First, a surrogate model was developed to replace the high-fidelity model of an FBI process, and utilized to determine a set of possible operating conditions that may lead to a target NOx emission concentration. Second, the candidate operating conditions were fed to the Aspen Plus™ process simulation program to determine the most economically competitive option with respect to its total cost. The developed framework can provide operational guidelines for a clean and economical incineration process of explosive waste.