The model-based method to define the optimal calibration maps for important diesel engine parameters may involve three major steps. First, the engine speed and load domain – in which the engine is ...

Variable smoothing of optimal diesel engine calibration for improved performance and drivability during transient operation

&lt;div&gt;Methanol is emerging as an alternate internal combustion engine fuel. It is getting attention in countries such as China and India as an emerging transport fuel. Using methanol in spark ignition engines is easier and more economical than in compression ignition engines via the blending approach. M85 (85% v/v methanol and 15% v/v gasoline) is one of the preferred blends with the highest methanol concentration. However, its physicochemical properties significantly differ from gasoline, leading to challenges in operating existing vehicles. This experimental study addresses the challenges such as cold-start operation and poor throttle response of M85-fueled motorcycle using a port fuel injection engine. In this study, M85-fueled motorcycle prototype is developed with superior performance, similar/better drivability, and lower emissions than a gasoline-fueled port-fuel-injected motorcycle. An open electronic control unit was installed using suitable wiring harness/sensors and actuators to control the engine. Then the motorcycle electronic control unit was calibrated for transient operations on a chassis dynamometer. The motorcycle was tested under road load simulation and wide-open throttle conditions on the chassis dynamometer to compare its performance with a baseline gasoline-fueled motorcycle. Evaluation parameters included power at wheels, maximum vehicle speed, and time-based and speed-based acceleration characteristics. Transient emissions were evaluated following the Indian driving cycle protocols. The effectiveness of the catalytic converter for M85 fueling was assessed by comparing various emissions upstream and downstream of the catalytic converter. M85-fueled motorcycle generated higher power at wheels and similar maximum speeds as baseline gasoline-fueled motorcycle. Fine-tuned M85-fueled motorcycle exhibited superior acceleration characteristics over baseline gasoline-fueled motorcycle, indicating that an appropriate tuning strategy could tackle the issue of “drivability.” M85-fueled motorcycle emitted lower carbon monoxide and hydrocarbon during the warm-up cycles in the Indian driving cycle protocol. The inherent fuel oxygen of M85 enhanced the carbon monoxide–carbon dioxide conversion, reducing carbon monoxide emissions in the engine exhaust. The existing catalytic converter was also suitable for M85 fueling since the hydrocarbon, nitric oxide, and carbon monoxide emissions were effectively reduced downstream of the catalytic converter in all test conditions.&lt;/div&gt; 

Methanol (M85) Port-Fuel-Injected Spark Ignition Motorcycle Engine
 Development—Part 2: Dynamic Performance, Transient Emissions, and Catalytic
 Converter Effectiveness

Abstract In the context of today’s dynamic environment, agility and speed are two essential characteristics that apply to project management in the software development industry, as well as in many other industries. In order to meet the complex and continuous challenges of the digital age, the principles, techniques and methods of Agile Project Management and Scrum are expected to become more widespread, especially in software development, replacing or augmenting the traditional ones. This paper aims to identify trends in project management related to digital transformation and diffusion of Industry 4.0 technologies. A bibliometric analysis was carried out by searching the WOS database. The resulting documents were exported and processed in VOSviewer to fulfil the scope. The research revealed that, in the context of digital transformation, information technology supports the agile approach, agile transformation and agile project management. Furthermore, emerging technologies specific to Industry 4.0, especially artificial intelligence, and big data, can contribute significantly to all project phases. These emerging technologies can improve data processing and analysis, project forecasting, and risks prediction, can support decision making thus contributing to the success of the project. 

Agile Project Management in the Age of Digital Transformation: Exploring Emerging Trends

Exploring Transparency in Decisions of Artificial Neural Networks for Regression

In the contemporary realm of industry, the imperative for influential and steadfast systems to detect anomalies is critically recognized. Our study introduces a cutting-edge approach utilizing a deep learning model of the Long-Short Term Memory variety, meticulously crafted for real-time surveillance and mitigation of irregularities within industrial settings. Through the careful amalgamation of data acquisition and analytic processing informed by our model, we have forged a system adept at pinpointing anomalies with high precision, capable of autonomously proposing or implementing remedial measures. The findings demonstrate a marked enhancement in the efficacy of operations, with the model’s accuracy surging to 95%, recall at 90%, and an F1 score reaching 92.5%. Moreover, the system has favorably impacted the environment, evidenced by a 25% decline in CO2 emissions and a 20% reduction in water usage. Our model surpasses preceding systems, showcasing significant gains in speed and precision. This research corroborates the capabilities of deep learning within the industrial sector. It underscores the role of automated systems in fostering more sustainable and efficient operations in the contemporary industrial landscape. 

Advancing smart city factories: enhancing industrial mechanical operations via deep learning techniques

Drivability Improvements on Electronic Diesel Engines

Forecasting System and Project Monitoring in Industry

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Drivability Improvements on Electronic Diesel Engines

Forecasting System and Project Monitoring in Industry