考虑压力约束的多杂质氢系统集成

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Abstract

由于原油的劣质化和重质化以及环保法规对油品质量的要求不断提高,使得炼油厂对氢气的需求在不断增加,合理利用炼厂氢气资源,优化配置氢气网络,对于节能减排意义重大。在氢气网络中,压力是一个重要约束,特别是氢气压缩机的投资很大,在氢气网络集成中,必须从经济性的角度满足压力的要求。为了在多杂质氢网络系统设计中同时考虑压力的影响,本文在多杂质赤字率夹点法的基础上,提出了考虑压力约束的多杂质氢系统的集成方法,在进行氢源和氢阱匹配时同时满足氢气浓度、杂质浓度和压力需求。通过引入系统最小压力降的概念,对氢源和氢阱按照杂质浓度进行排序后,构造氢源和氢阱的平均压力一流量图,从而可以判断各区域压力是否足够。当压力不足时,可通过改变公用工程用量或设置压缩机来满足压力需求。建立了费用公式来决定是改变公用工程用量还是设置压缩机,以最终确定经济合理的网络匹配。实例计算表明,所提出的方法是有效的。

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Key words

multiple impurities,hydrogen network,process integration,pressure constraint

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Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

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