Deep neural network-based optimal selection and blending ratio of waste seashells as an alternative to high-grade limestone depletion for SO_X capture and utilization

In wet flue gas desulfurization system, the resource depletion of high-grade limestone, used as conventional SO_x absorbent, is becoming serious for SO_x capture and utilization. This paper proposes optimal selection and blending ratio of waste seashells as an alternative to high-grade limestone depletion using a deep neural network (DNN)-based surrogate model. Cost optimization proceeds as follows: data generation, data preprocessing, development of DNN-based surrogate model, and derivation of cost optimal selection and blending ratio. First, a process model is developed to generate the datasets, which are gypsum purity according to selection and blending ratio of each seashell and limestone. In addition, a mathematical model is proposed to calculate the total annualized cost (TAC) considering the pretreatment cost of seashell, and the TAC is added to the datasets to predict the gypsum purity as well as TAC. Second, the generated datasets are preprocessed to intensify prediction performance of the DNN-based surrogate model using the z-score normalization method. Third, a DNN-based surrogate model is developed to predict the gypsum purity and TAC according to the selection and blending ratio. Finally, the cost optimal selection and blending ratio are derived from 2.4 billion data generated by the developed DNN-based surrogate model under two constraints: gypsum purity and total SO_x absorbent consumption. As a result, the derived selection and optimal blending ratios are low-grade limestone (80.86%), oyster shells (10.78%), scallop shells (0.216%), cockle shells (0.323%), clam shells (2.426%), and mussel shells (5.391%), reducing the TAC by US$788,469.