Life Cycle Assessment and Multiobjective Optimization for Steam Cracking Process in Ethylene Plant

With energy savings and emission reduction becoming national policies in recent years, the environmental impacts of industrial production are more and more critical. Most of the studies have concentrated on the environmental effects of the industrial production process. Little attention has been paid to the energy consumption and pollution emission in extracting, processing, and transporting the feedstock and other secondary materials. An integrated multiobjective optimization framework is proposed for the steam cracking process on the basis of a life cycle assessment and data-driven modeling methods. A multiobjective economic–environmental optimization model is developed on the basis of industrial and simulated data. A multiobjective optimization model combined with energy cost is also developed for comparative study. The nondominated sorting genetic algorithm-II is utilized to solve the problems, and the Pareto front is obtained. An industrial case study is carried out to indicate the effectiveness of the proposed method. The results show that the LCA-based method can better represent the environmental impacts in comparison with the standard energy cost model. Therefore, the proposed method can achieve a better tradeoff between economic benefits and environmental impacts for guiding ethylene production

Additional file 1 of CD7-directed CAR T-cell therapy: a potential immunotherapy strategy for relapsed/refractory acute myeloid leukemia

Additional file 1: Figure S1. Cytotoxicity and cytokines analysis of the CD7 CAR T-cells. Figure S2. Diagrammatic sketch of the treatments and response. Figure S3. Infusion-related hepatic toxicities. Figure S4. Flow cytometry analysis of the fraction of T-cells and NK cells in the PB after infusion. Figure S5. Flow cytometry of the T-cell fractions in the PB after infusion of CART cells. Table S1. The result of SNP array (Cytoscan 750K/HD) at diagnosis. Table S2. A panel of 222 genes detected by next-generation sequencing. Table S3. A panel of targeted transcriptome RNA sequencing (RNA-seq)