Open Access
Article
09 May 2025Modeling and Assessing Economical Feasibilities for Waste to Energy Conversion/Incineration Process in Context of Municipal Solid Waste
At the time of the study, most of the municipal waste, including solid municipal waste, in the city of St. Petersburg and in the connected larger Leningrad region is processed by landfilling. This sort of waste processing in open landfills causes environmental damage, uncontrollable landfill fires, bad and dangerous odors, nearby rivers/streams, groundwater pollution, CH4 and CO2 emissions, to mention a few. Additionally, landfilling is a waste of energy and material resources present in the content dumped into landfills. In this context, Waste-to-Energy (WtE) incineration is a process that we use to recover the energy the materials have back to usable form, which we use in the form of heat and electricity. Even though a lot of resources and energy are available in the (municipal solid) waste, it does not mean that recovering it would always make sense. Our study analyses and estimates the profitability of a WtE incineration plant(s) in the city of St. Petersburg and the connected Leningrad region. With the available data and following analysis, we have concluded that the WtE incineration is economically feasible in this specific region and city areas, given that the implementations follow more traditional (economically less expensive and easier) technical and process model solutions. As a note of results stability, it needs to be pointed out that the changes in estimates of gate fees, cost of electricity and heat, and so on do impact the economic feasibility a lot, and larger scale changes in the assumed revenues would have a high impact on the outcome of repeatability of the results.
Open Access
Article
28 May 2025Mechanisms of Machine Vision Feature Recognition and Quality Prediction Models in Intelligent Production Line for Broiler Carcasses
With global broiler production reaching 103 million tons in 2024—a 1.5% increase over 2023—the poultry industry continues to grow rapidly. However, traditional broiler segmentation methods struggle to meet modern demands for speed, precision, and adaptability. First, this study proposes an improved lightweight image segmentation algorithm based on YOLOv8-seg and integrates the Segment Anything Model (SAM) for semi-automatic annotation, achieving precise mask segmentation of broiler parts. Subsequently, Key geometric features (e.g., area, perimeter, axes) were extracted using image processing techniques, with enhancements from HSV color transformation, convex hull optimization, and ellipse fitting. Furthermore, Image calibration was applied to convert pixel data to physical dimensions, enabling real-sample validation. Using these features, multiple regression models—including CNNs—were developed for carcass quality prediction. Finally, by analyzing the broiler segmentation process, machine vision techniques were effectively integrated with quality grading algorithms and applied to intelligent broiler segmentation production lines, providing technical support for the intelligent and efficient processing of poultry products. The improved YOLOv8-seg model achieved mAP@0.5:box scores of 99.2% and 99.4%, and the CNN model achieved R2 values of 0.974 (training) and 0.953 (validation). Compared to traditional systems, the intelligent broiler cutting line reduced failure rates by 11.38% and improved operational efficiency by over 3%, offering a reliable solution for automated poultry processing.
Open Access
Article
05 June 2025Enhancing Product Development Excellence through Quality Management Tools: A Comprehensive Review and Integrated Conceptual Framework
In today’s rapidly evolving and highly competitive global markets, achieving product development excellence is critical for organizations striving for sustained growth and customer-centric innovation. This study highlights the integral role of key quality management tools in enhancing product development processes, reducing defects, and driving continuous improvement. It presents a robust methodology that strategically combines Quality Function Deployment (QFD), Failure Mode and Effects Analysis (FMEA), and the DMAIC (Define, Measure, Analyze, Improve, Control) framework to significantly improve the quality, reliability, and efficiency of product development efforts. Built on core principles of customer-centricity, innovation, cross-functional collaboration, continuous improvement, and risk-based thinking, the methodology emphasizes capturing the Voice of the Customer (VoC) and identifying Critical-to-Quality (CTQ) attributes to align product outcomes with customer expectations and business objectives. Utilizing the DMAIC framework, the organization systematically drives process optimization and innovation throughout the product lifecycle Key Performance Indicators (KPIs) are established to track efficiency, quality, customer satisfaction, and time-to-market, while Agile methodologies enhance flexibility, speed, and responsiveness. The study further identifies organizational, technical, cultural, and managerial barriers to product development excellence and proposes targeted strategies to address them and ensure sustainable success. This integrated framework fosters a culture of innovation and continuous learning, enabling organizations to anticipate challenges, manage risks, and consistently deliver superior product development outcomes. While currently conceptual, the framework is slated for empirical validation through case studies, pilot projects, and simulations to verify its practical applicability across diverse development contexts.
