This study investigates the effects of multistage compression on single mixed refrigerant processes in terms of specific work. Comparison of specific work published in the literature is not straightforward due to the variety of compression configurations and the design bases. Therefore, four configurations (two-, three-, and four-stage and pump-added three-stage compressions) along with three natural gas compositions were considered. To compare with the simulation and optimization results in the literature, these 12 cases, having the same design basis, were optimized by adjusting the optimization variables such as the flow rate and composition of the refrigerant, the compression ratio of each compressor, the inlet pressure of the first compressor, and the outlet temperatures of the hot and cold refrigerant streams. There were two important findings: (1) adding a pump reduces specific work more than adding a compressor or decreasing the minimum temperature difference value in the compressors; (2) among the four configurations, the refrigerant composition does not significantly change, although it greatly affects the efficiency. The former results from the compressor constraint of the gaseous inlet and the latter from the minimum temperature constraint of the multistream heat exchanger. Furthermore, direct comparisons to other studies were also performed showing the importance of optimization and the effect of the design basis.

Natural gas liquefaction is an energy-intensive process in which energy reduction is a main concern. This research focused on minimizing the energy of the pure refrigeration cycle in natural gas liquefaction by improving the subcooling system. To minimize energy consumption, a pure refrigeration cycle with a subcooling system was simulated, and the result was thermodynamically analyzed. The thermodynamic analysis identified an opportunity to reduce the energy consumption, and a new design was proposed for the subcooling system. In addition, the proposed design was deterministically optimized to find the optimal compressing ratio, temperature, pressure, and flow rate. As the result, the optimal operating conditions were determined, and the energy consumption was reduced by 17.74%.

Distillation is an energy‐consuming process in the chemical industry. Optimizing operating conditions can reduce the amount of energy consumed and improve the efficiency of chemical processes. Herein, we developed a machine learning‐based prediction model for a distillation process and applied the developed model to process optimization. The energy consumed in the distillation process is mainly used to control the temperature of the distillation column. We developed a model that predicted temperature according to the following procedure: (1) data collection; (2) characteristic extraction from the collected data to reduce learning time; (3) min–max normalization to improve prediction performance; and (4) a case study conducted to select the artificial neural network algorithm, optimization method, and batch size, which are the most appropriate elements for predicting production stage temperature. The result of the case study revealed that the most appropriate model was observed with a root mean squared error of 0.0791 and a coefficient of determination of 0.924 when the long short‐term memory algorithm, Adam optimization method, and batch size of 128 were applied. We calculated the amount of steam consumption required to consistently maintain the production stage temperature by utilizing the developed model. The calculation result indicated that the amount of steam consumption was expected to be reduced by approximately 14%, from an average flow rate of 2763–2374 kg/h. This study proposed a control method applying a machine learning‐based prediction model in the distillation process and confirmed that operation energy could be reduced through efficient operation.

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The physical properties required in polypropylene composites (PPCs) vary depending on the purpose of use. In the manufacturing of PPCs, it is crucial to determine the types and quantities of numerous reinforcements to meet the required physical properties. Owing to industrial complexity, most PPC manufacturers produce the composites repeatedly until the desired physical properties are obtained. Hence, to reduce trial and error, we developed prediction models for the physical properties of PPCs based on commercial recipe data. The recipe data included information about five physical properties of composites manufactured using 90 materials. In complex industrial environments, because one recipe is usually composed of 2–12 materials, numerous combinations of data sets are created. It causes the lack of the same material combination data sets and thus makes it difficult to develop a good performance model. Therefore, a novel categorization process is suggested as data preprocessing to overcome the data imbalance problem. The models for predicting the five physical properties (flexural strength, melting index, tensile strength, specific gravity, and flexural modulus) were developed using random forest, and the performance of the prediction models was improved via hyperparameter optimization. Furthermore, the effects of the materials on the performance of the models were numerically described through variable importance analysis. Finally, a software was developed to implement the prediction models in the industry. The software was applied to a commercial composite and achieved high accuracy, demonstrating the effectiveness of this study. Thus, the software suggests decision‐making solutions to save cost and time by reducing the trial and error in the industrial environment with high complexity.

An empty fruit bunch (EFB) is a byproduct of the palm oil production process with an undried moisture content of 60−70%, which is too high for use as direct combustion fuel. Drying processes are generally considered essential for the recent use of EFBs as power plant fuels because their high moisture content decreases the boiler efficiency. The lower moisture content of dried EFBs increases the heating value and boiler efficiency but creates a trade-off with the energy required for the drying process. This study developed an EFB-based 10 MW power plant model by integrating economic evaluations in order to obtain optimal drying conditions. A hot air dryer was used in the drying process. The EFB evaporation behavior was predicted by reflecting the drying kinetics of EFBs in Aspen Plus. The optimum drying conditions were found to be a steam recirculation ratio of 0.25 and drying time of 23 min, creating dried EFBs with a 9.91% moisture content, which reduced costs by 5.48% relative to the undried base scenario. In addition, the developed model was compared to the drying process of a real power plant currently under construction in Indonesia. This drying process reduces the EFB moisture content from 48 to 20%.

Environmental issues, the growing demand for energy, political concerns, increasing crude oil prices and the medium-term depletion of petroleum created the need for the development of vegetable oils as alternative fuels. Vegetable oil-based fuels (bio fuels) are promising alternative fuels for diesel engines because of their environmental and strategic advantages. To design equipment for biofuel production and an optimizing process for biodiesel production, their thermophysical properties must be known. In this work, the Jatropha curcas biodiesel was prepared, and thermophysical properties, densities (ρ12), and speed of sound (u 12) for J. curcas biodiesel (1) + diesel fuel no. 2 (2) binary mixtures were measured as functions of composition at temperatures ranging from T = (288.15 to 308.15) K and atmospheric pressure. The observed data have been utilized to evaluate the excess molar volume, V 12 E, of this binary mixture. This binary mixture (blend) exhibits a temperature-dependent behavior, and densities decrease linearly with temperature.

This study focused on finding optimal procurement and production planning including corrosion effect. We developed the corrosion forecasting and cost model, which are combined in the procurement and production planning model. The corrosion cost model was presented to calculate cost depending on corrosion rate estimated by corrosion factors, which are H+, Cl–, H2S, and CO2. This study found the optimal results such as the optimal amount of crude to be purchased and optimal design. Several case studies are also included in this study to analyze and improve the optimization production planning model of petrochemical and refinery processes. Although the government regulation requires the operating cycle time as 4 years, this study reveals that the profit per day is increasing up to 5 years if the corrosion is considered. This result increases the net profit per day about 4.14% in a crude distillation unit (CDU) process with 120 000 barrels per day capacity.