American Chemistry Council Honors Chemical Engineer with Responsible Care Award
A PhD candidate in the Department of Chemical and Biomolecular Engineering was selected by the American Chemistry Council (ACC) to be the recipient of the Responsible Care Artificial Intelligence and Digitalization Award. Naji Alani, who is advised by Mohamad Al-Sheikhly, a professor in materials science and engineering, received the award for this year, which recognizes initiatives that utilize artificial intelligence technology to advance safety and environmental performance. As a Data Science Innovation Project Leader at Dow, Alani received the award for his contributions to the company during ACC’s 2025 Responsible Care and Sustainability Conference in Fort Lauderdale, Florida. “This award recognizes my commitment in advancing the performance of environmental operations in the chemical industry. My chemical engineering background has equipped me with the skills needed for building and deploying cutting-edge and data-centric approaches to tackle industry-wide challenges,” said Alani. The project gained recognition after he successfully implemented an AI-based solution to identify incoming noncompliance events, manage risks and eliminate environmental impacts related to Total Dissolved Solids—a measure of organic and inorganic substances in water. This metric is used in wastewater management to ensure that discharged water meets regulatory standards. This solution is thought to advance traditional wastewater forecasting methods by relying on a data-driven approach for proactive management. The digital tool has maintained compliance for over three years, significantly enhancing environmental conservation at Dow. The technical approach involved noise reduction, feature engineering and automation tools for real-time insights and visualization, which positioned the tool as a broader resource for industrial applications. Under the guidance of Al-Sheikly, Alani studies optimization in science and engineering, focusing on addressing sustainability and materials discovery using machine learning. His doctoral thesis, titled “Machine Learning Optimization & Control for Sustainability of Process Systems Engineering,” seeks to advance fault detection and diagnostics methods for chemical manufacturing processes. His goal is to restructure industrial sustainability practices from a reliability perspective and establish a formal connection between these two concepts. More specifically, the doctoral candidate studies deep learning methods for active gathering of actionable system intelligence that can keep chemical processes safely operational and reliably sustainable. He thanks his advisor, professor Al-Sheikhly, for the accomplishment. “Reaching to this milestone was not a matter of if, but when with the unwavering support from professor Al-Sheikhly. The mission is not over yet. Our innovative approach will continue tackling the challenges of chemicals manufacturing one datapoint at a time,” he said.
May 20, 2025 Prev Next |
