DIEGO RAMON BONILLA GARCIA

Dr. DIEGO RAMON BONILLA GARCIA

orcid

Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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12 November 2023 in Energies
Verified
Journal Article
Open access
PDF file
DGDIEGO RAMON BONILLA GARCIA
DIEGO RAMON BONILLA GARCIA
MSMargarita Gil Samaniego
Margarita Gil Samaniego
Conrado García-GonzálezConrado García-González
Conrado García-González
Armando PerezArmando Perez
Armando Perez
MCMarcos A. Coronado
Marcos A. Coronado
Water supply represents a significant electrical load worldwide. The operation of inefficient pumps contributes to increased energy demand and carbon footprint. Current methods for evaluating industrial water pumps present technical and economic challenges that need to be addressed. For these reasons, we developed a practical and cost-effective virtual instrument called the Pumping Evaluation System (PES). The PES tool performs real-time monitoring of electrical, hydraulic, and efficiency data, as well as the Operating Characteristic (OC) curves. The PES tool was implemented on a vertical pump currently used by a municipal water distribution plant in Mexicali, Mexico. A comparative analysis between the experimental data and the manufacturer’s data revealed potential energy savings of 361,455 kWh/year (24.35%) and an equivalent carbon footprint reduction of 157,233 kg of CO2eq/year if the old pump is replaced by a new one, which is the lowest cost alternative according to a Life Cycle Cost (LCC).

30 June 2026 in Eng
Verified
Journal Article
Open access
PDF file
RORafael Ortiz
Rafael Ortiz
Fabian N. Murrieta-RicoFabian N. Murrieta-Rico
Fabian N. Murrieta-Rico
GTGabriel Trujillo-Hernández
Gabriel Trujillo-Hernández
DGDIEGO RAMON BONILLA GARCIA
DIEGO RAMON BONILLA GARCIA
JAJosé Ramón Ayala
José Ramón Ayala
MRMaría E. Raygoza-L.
María E. Raygoza-L.
Buildings and construction account for nearly one-third of global energy demand, making the thermal performance of building envelopes an important factor in reducing heating and cooling loads. Conventional steady-state indicators are useful for comparison, but they do not always describe how complete assemblies respond to changing outdoor conditions. This study presents a dynamic experimental method for evaluating construction assemblies using energy compensation under controlled indoor conditions. A highly insulated test module was used to maintain a stable indoor temperature while measuring the electrical heating energy required to compensate for heat losses through interchangeable assemblies exposed to outdoor temperature variations. Three systems were tested: double gypsum board, hollow concrete block, and externally insulated hollow concrete block. To compare the assemblies under different outdoor conditions, corrected heating energy was normalized by test area and heating degree-hours (HDHs), resulting in a Dynamic Thermal Performance Index (DTPI). The DTPI decreased from 52.4 Wh/(m2·°C·h) for the double gypsum board assembly to 21.5 Wh/(m2·°C·h) for the externally insulated hollow concrete block, while the mean corrected heating energy decreased from 2.23 to 0.91 kWh/day. The gypsum board assembly showed the fastest thermal response and the highest energy demand, whereas the externally insulated block provided the most stable indoor-side behavior by reducing the effect of outdoor fluctuations on the concrete mass. The method links transient thermal behavior with measured energy consumption and offers a practical complement to conventional steady-state metrics for comparing building envelope systems.

6 March 2026
Verified
Book Chapter
Closed access
MRMaría E. Raygoza-L.
María E. Raygoza-L.
GTGabriel Trujillo-Hernández
Gabriel Trujillo-Hernández
DGDIEGO RAMON BONILLA GARCIA
DIEGO RAMON BONILLA GARCIA
Fabian N. Murrieta-RicoFabian N. Murrieta-Rico
Fabian N. Murrieta-Rico
This chapter explores the importance of resource management, circular economy, and food sovereignty as strategies for transforming the global agri-food system. Food production contributes approximately one-third of global greenhouse gas emissions, estimated at 17.318 billion metric tons of CO2 equivalent per year 57% of which originates from animal-based foods, 29% from plant-based products, and 14% from other uses. In response to the dominant linear model characterized by waste, corporate concentration, and ecological degradation a regenerative approach is proposed, based on resource reuse and agroecological diversification. Through an integrated analysis, the chapter addresses the environmental impacts of food waste and the potential of circular technologies, artificial intelligence, and renewable energy to build more resilient food systems. It concludes with key recommendations for stakeholders aimed at consolidating a circular and sovereign food economy.

22 January 2025
Verified
Book Chapter
Closed access
MRMaría E. Raygoza-L.
María E. Raygoza-L.
JOJesus Heriberto Orduño-Osuna
Jesus Heriberto Orduño-Osuna
GTGabriel Trujillo-Hernández
Gabriel Trujillo-Hernández
DGDIEGO RAMON BONILLA GARCIA
DIEGO RAMON BONILLA GARCIA
AMAbelardo Mercado
Abelardo Mercado
Fabian N. Murrieta-RicoFabian N. Murrieta-Rico
Fabian N. Murrieta-Rico
This chapter explores the transformative potential of artificial intelligence (AI) in STEM education, focusing on its role as a driver of socioeconomic development in emerging economies. By integrating AI with STEM disciplines, educational processes can be optimized, personalized, and made more inclusive, thereby reducing educational gaps and preparing future generations to face global challenges. From a psychological perspective, the chapter examines how AI enhances learning and memory, contributing to deeper cognitive engagement. It also addresses the challenges of AI implementation, including technological, economic, and ethical barriers, highlighting the importance of public policies that promote equitable and sustainable growth, the challenges and opportunities in R&D projects, and how the development of emerging economies requires further exploration of AI's potential to innovate in STEM fields and its broader implications for socioeconomic development.