In the meantime, governments worldwide have already consented to a sustainable development goal. A business organization strives to achieve global goals by creating economic value based on profit creation, social and environmental responsibilities as a triple bottom line of sustainability.
Facing the challenges, managing their resources with data analysis, i-APC's Process Performance Improvement is one of the most considering strategies to improve overall operational performance of their production facilities and Energy Optimization, which minimizes energy consumption and maximizes fuel efficiency is also included. Simply say, the innovative service from i-APC is a straightforward solution with pragmatic design to drive the economic goals or a financial value accordingly.
Moreover, the organization can create a movement to meet UN sustainable development goals (SDGs) by enhancing intelligent systems and implementing “Item No.9 Industry, Innovation, and Infrastructure” or “item No.12 Ensure Sustainable Consumption and Production pattern” into the transformative change.
In terms of environmental responsibility, i-APC delivers not only a product transition loss and also product quality variation minimization at minimum vent to reduce carbon emissions.
Regarding social responsibilities, i-APC is launching the green manufacturing support program for environmentally friendly organization to improve their plant operation such as,
i-APC believes that social and environmental responsibilities are everyone’s collaboration. Mapping the solution potentially in the value chain is a compellingly beneficial opportunity.
Per i-APC approach, there are three highly efficient outcomes to meet sustainability goals
The data analytic, optimization and prediction process enhance planning and schedule of unit operation. In terms of resources management, human intervention workload is minimized by AI while other consumables (e.g., chemical, energy) and waste generation are noticeably reduced.
The intelligent advanced control and optimization solution reduces process variability. The reactor's temperature, the flow rate of the chemical, and real-time control optimization consequence lower energy usage and increased production rate simultaneously with more consistent product quality.
The predictive solution from machine learning forecasts the reactor's failure, which avoids unplanned production and prolong turnaround cycle to increase competitiveness towards global demand & supply.