Veja como você pode aplicar o pensamento crítico para melhorar a resolução de problemas em projetos de Engenharia Química.

Narrow your focus and define the scope by considering what aspect you are discovering more about. Prepare for unknowns and be as specific as possible. What are some potential anticipated or unplanned outcomes? Consider unknown information and add this to your defined problem, you can return to your definition and iterate over it if improvement is needed.

Plan how you will document your findings. This will help when assessing data and gather feedback on for improving your process. Take ample time for your process, and iterate over your data to ensure accuracy. Determine how you will collect data and how you will remain unbiased in doing so.

Conduct a thorough comparative analysis of each option, weighing factors such as cost, implementation time, environmental impact, and technical feasibility. Where possible, pilot these solutions on a smaller scale to identify potential risks and challenges before full-scale deployment. For example, if you're facing inefficiencies in a distillation process, don't just rely on standard methods like increasing pressure or temperature. Instead, explore advanced options like integrating membrane separation technology or employing process intensification techniques. By analyzing these alternatives, you might discover that a hybrid system combining traditional and modern approaches yields better efficiency and reduces energy consumption.

Use whiteboarding and flow charts to develop answers to your objectives. To begin, start with a simple solution. Once you have something, work towards improvement. When you have one pathway, do not stop there. Spend time thinking about what you are missing and talk through how you are achieving your objectives. After some time has passed, reassess your solutions and work to identify gaps in them. Test, retest and iterate over your answers to achieve the results you find in order to strengthen your findings and ensure you have found the right solution.

In designing experiments or simulations to test hypotheses, we need to have a clear idea of what data we want to gather, how that data will be analyzed, and what different results do to inform our hypothesis. Simply asking “what if” questions will not result in a systematic approach or solution to emerge. We often use design of experiment approaches to sample the design space and guide further investigations. Rather than trying to find the one answer or optimal solution the first time, rationally exploring a design space can lead to a more complete picture in less time and guide future work.

Start by clearly defining your hypothesis and outlining the expected outcomes based on your assumptions. Ensure that your hypothesis is measurable and specific, allowing the results to provide clear insights into whether your assumptions hold true. Design experiments or simulations that replicate real-world conditions as closely as possible, whether through small-scale laboratory tests, computer modeling, or pilot projects, depending on the problem. Carefully control variables to isolate the effects of the hypothesis being tested. For example, if you hypothesize that adjusting the pH level in a reaction will improve yield, ensure that other variables, such as temperature and pressure, remain constant during your tests.