Search

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. [ABSTRACT FROM AUTHOR]

In this work, alternative evaporation processes for PLA production were designed with economic assessment. The suggested processes are the multiple-effect evaporation (MEE) process and thermal vapor recompression (TVR)-assisted evaporation process. First, the MEE process can efficiently reuse waste heat by additional column installation, thereby reducing the steam energy consumption. The proposed MEE process involves five columns, and after the evaporation in each column, the waste heat of the emitted vapor is reused to heat steam in the reboiler of the next column. Second, the suggested TVR-assisted evaporation process utilizes an additional steam ejector and recovers waste heat from the emitted vapor by increasing the pressure using high-pressure driving steam at the steam ejector. Each alternative process was modeled to predict the steam energy consumption, and to determine the cost-optimal process; the total annualized cost (TAC) of each alternative process was calculated as evaluation criteria. In the simulation results, the alternative processes using MEE and TVR reduced the steam consumption by 71.36% and 89.97%, respectively, compared to the conventional process. As a result of economic assessment, the cost-optimal process is the alternative process using TVR and the TAC can be decreased by approximately 90%. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]