This article discusses the potential applications of the Fuel Shot liquid catalyst in compression ignition (CI) engines for reducing toxic substances in exhaust gases. Incorporating catalysts into fuel can optimize the combustion process, consequently reducing the emission of toxic substances into the atmosphere. Toxic compounds, such as nitrogen oxides, particulate matter, and hydrocarbons, adversely affect flora and fauna. Various methods are known for reducing their concentration in engine exhaust gases, one of which is the Fuel Shot liquid catalyst. The authors conducted experiments on a Fiat 1.3 JTD engine with a Common Rail system. The results indicate that the application of the liquid catalyst reduces the content of nitrogen oxides and hydrocarbons in the exhaust gases and slightly decreases fuel consumption. Additionally, investigations were carried out on the engine’s injection apparatus, which was fueled with modified fuel. The findings demonstrate that the fuel additive does not affect the wear of precision parts of fuel injectors and high-pressure pumps.

The paper addresses what we term the "Impedance Mismatch" inherent in multi-energy microgrids—namely, the conflict where disparate physical domains traditionally force the use of isolated or highly rigid Supervisory Control and Data Acquisition (SCADA) data silos. We present a scalable, open-source edge-to-cloud telemetry pipeline orchestrated entirely through a custom MING+React stack (Mosquitto, InfluxDB, Node-RED, Grafana). The core architectural contribution is a category-specific Canonical Data Model (CDM) that functions as a translation layer, effectively decoupling sensor hardware from ingestion logic. Coupled with an automated Null-Pruning middleware loop and Metadata Separation, this architecture guarantees strict input purity prior to time-series persistence. To validate the system, the middleware was subjected to a high-fidelity stochastic simulation utilizing a Strict Corridor Algorithm to mimic physical inertia across 12 distinct energy vectors. Simulation-based validation confirms that the pipeline successfully maintains data integrity for valid telemetry packets, achieves high accuracy in pruning malformed data, and operates with low latency under concurrency. These findings demonstrate the architecture's feasibility as a resilient, cross-domain research environment for modern Digital Learning Factories.

This applied systems-integration study addresses the “Impedance Mismatch” inherent in multi-energy microgrids—namely, the conflict where disparate physical domains traditionally force the use of isolated or highly rigid Supervisory Control and Data Acquisition (SCADA) data silos. We present a scalable, open-source edge-to-cloud telemetry pipeline orchestrated entirely through a custom MING+React stack (Mosquitto, InfluxDB, Node-RED, Grafana). The core architectural contribution is a category-specific Canonical Data Model (CDM) that functions as a translation layer, effectively decoupling sensor hardware from ingestion logic. Coupled with an automated Null-Pruning middleware loop and Metadata Separation, this architecture maintains high input purity prior to time-series persistence. To validate the system, the middleware was subjected to a high-fidelity stochastic simulation utilizing a Strict Corridor Algorithm to mimic physical inertia across 12 distinct energy vectors. Simulation-based validation demonstrates that under simulated conditions, the pipeline maintains data integrity for valid telemetry packets, achieves high accuracy in pruning malformed data, and operates with low latency under concurrency. These findings demonstrate the feasibility of this applied architecture as a resilient, cross-domain research environment for modern Digital Learning Factories.