Uno de los equipos mayores, presentes en las plantas de refinación de petróleo y petroquímica es el horno de calentamiento de fluidos. El control de costos de operación de los combustibles derivados del petróleo que constituyen un recurso agotable han hecho tomar conciencia de la importancia de una evaluación eficiente de los equipos y su optimización constituyen dos funciones muy importantes para los ingenieros de procesos.

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This research will address air pollution, a severe problem in all world cities, because it negatively affects people's health and deteriorates the ecosystem. NO2 is a gas linked to acid rain formation and various reactions with greenhouse gases. Meteorological variables influence the behavior of tropospheric NO2 concentration. During the period of confinement due to the COVID-19 pandemic, the concentration levels of pollutants dropped abruptly, which meant relief for the ecosystem. The application of Time Series models allows us to graphically identify the concentration of contaminants in various areas and make accurate forecasts to mitigate environmental problems in the future. The research analysis shows that the SARIMA model effectively forecasts the pollutant concentration in the San Borja and San Martin de Porres districts in Lima. Error tests such as R2, MAE, MAPE, MSE, and RSME, a...

One of the main causes of having low crop efficiency in Peru is the poor management of water resources; which is why the main objective of this article is to estimate the amount of irrigation water required in quinoa crops through a comparison between the machine learning and AquaCrop models. For the development of this study, meteorological data from the province of Jauja and descriptive data of quinoa crops were processed and a simulation period was established from June to December 2020. From the simulation carried out, it was determined that the best model to predict the required irrigation water is the Adaptive Boosting (AdaBoost) model in which it was observed that the mean and standard deviation of the AdaBoost models (mean = 19.681 and SD = 4.665) behave similarly to AquaCrop (mean = 19.838 and SD = 5.04). In addition, the result of ANOVA was that the AdaBoost model has the best ...

The dengue virus has become an increasingly critical problem for humanity due to its extensive spread. This is transmitted through a vector that sprouts in certain climatic conditions (tropical and subtropical climates). The transmission of the disease can be associated with certain climatic variables that reinforce the outbreak. Data were collected on dengue cases by epidemiological week registered in Loreto-Peru from January 1, 2016, to January 31, 2022. Likewise, data on meteorological variables (maximum and minimum temperature; dry and humid bulb temperature; wind speed and total precipitation in the area). In this study, four Machine learning modeling techniques were considered: Support Vector Machine (SVM), Decision Tree, Random Forest and AdaBoost; and the parameters defined to evaluate the models are: Accuracy, Precision, Recall and F-1. As a result, optimal AUC values were obtai...

This research will address air pollution, a severe problem in all world cities, because it negatively affects people's health and deteriorates the ecosystem. NO2 is a gas linked to acid rain formation and various reactions with greenhouse gases. Meteorological variables influence the behavior of tropospheric NO2 concentration. During the period of confinement due to the COVID-19 pandemic, the concentration levels of pollutants dropped abruptly, which meant relief for the ecosystem. The application of Time Series models allows us to graphically identify the concentration of contaminants in various areas and make accurate forecasts to mitigate environmental problems in the future. The research analysis shows that the SARIMA model effectively forecasts the pollutant concentration in the San Borja and San Martin de Porres districts in Lima. Error tests such as R2, MAE, MAPE, MSE, and RSME, a...

This research analyzes the demand for hair care products during the COVID-19 pandemic. Two forecasting models, Arima and Sarima, based on Machine Learning technology, were proposed to improve data analysis and supply chain management. The results showed that the SARIMA model had higher mean absolute error levels than the Arima model. The study also analyzed the demand for four hair dyes using statistical models, finding that three had seasonal demand. The SARIMA model accurately predicted demand for most hair dyes except one. Errors in the predictions were measured using different indicators, and the SARIMA model had lower error levels than the Arima model. The study's results were validated and compared with previous research, showing that the SARIMA model predicted the demand for hair dyes. Overall, this study highlights the usefulness of Machine Learning models in demand analysis and ...

This study focuses on developing a solution to one of the main problems in the food sector, product deterioration, often due to poor inventory management, low turnover, and lack of shelf-life control, among other causes. Therefore, this study is based on the design of a lean inventory management model proposed to reduce the number of deteriorated products in an egg product company in Peru, based on the analysis of the problem within the company and the study of previous research. As a result, the proposed method uses the tools of Machine Learning, Material Requirement Planning (MRP), 5S, and First Extended First Out (FEFO), reducing the main problem by 65.57% and the demand forecast error by 47.21%, thus reducing one of the leading root causes of the main problem. Thanks to this improvement, this research can contribute knowledge so that other companies with similar issues can implement ...

hbedon@ulima.edu.pe;ccarhuan@ulima.edu.pe;ygarcia@ulima.edu.pe;esaetton@ulima.edu.pe

The use of manual methods to forecast demand in perishable food companies is generally subject to the variability of internal and external factors in the company, causing excess inventories and significant monetary losses, so it is relevant to carry out this research with the objective of to demonstrate that by implementing Machine Learning it is possible to improve the accuracy of the demand forecast. A case study in a company in the poultry sector in Peru, forecasting the last quarter of 2022, based on a real sales database and applying the time series method, comparing the results of the Machine Learning model, and obtaining as a result in a model with high Forecast Accuracy (FA) of 97.56% and a high Forecast Bias (FB) of 2.44%. The research is an important contribution to knowledge, demonstrating that Machine Learning is an ideal tool to project the demand for perishable food product...

Time association data has been critical to the exploration field of paddy yield forecast. At durations the path of recent many years, countless flossy legitimate time arrangement. For this reason, this paper canters round searching forward to statistics esteems on a huge variety of flossy precept calculations. To clarify the approach in the course of gauging, the verifiable statistics of paddy yield. The method for acknowledgment used at some point of this exam can also be an extreme information grouping. The technique joins the coaching capacities of fake neural device with the human like data portrayal and clarification capacities of flossy precept frameworks and furthermore a trendy primarily based in maximum instances hold close framework. It's miles for the most half of used in Brobdingnagian expertise getting equipped applications. As we have a tendency to in all opportunity am awa...