Anomaly detection is essential for keeping chemical plants safe and running efficiently. Although many deep-learning methods have been proposed, most are still tested mainly on synthetic benchmarks such as the Tennessee Eastman Process (TEP). While these simulators enable fair comparisons, they do not reflect the noise, complexity, and irregular fault behavior of real industrial plants. As a result, it remains unclear how well these models generalize in practice. In this work, we extend our earlier ESCAPE study and move beyond water systems to industrially relevant chemical processes. We analyze data from two continuously operated pilot plant scenarios at the Technical University of Munich: n-butanol/water heteroazeotropic distillation and poly(oxymethylene) ether purification. We published these datasets for the first time at NeurIPS 2025. In this work, 30 anomaly detection methods, including 26 deep-learning and 4 classical approaches, are benchmarked using the open-source TimeSeAD l... [more]

A generally applicable framework for the evaluation of the sustainability of distillation processes is proposed by aligning indicators directly to selected sustainable development goals (SDGs) created by the United Nations. The indicators are related to the goals good health and well-being (SDG 3), clear water and sanitation (SDG 6), affordable and clean energy (SDG 7), decent work and economic growth (SDG 8), industry, innovation and infrastructure (SDG 9), responsible consumption and production (SDG 12), climate action (SDG 13) and life below water (SDG 14). A total of 12 sustainability indicators, including human toxicity potential, wastewater generation, water consumption, renewable energy share, energy demand, material footprint, profit, waste generation, recycling ratio of waste, greenhouse gas emission, eutrophication potential and acidification potential are assigned to selected SDGs. The application of the indicators is illustrated by two case studies: a batch (BD) and a conti... [more]