Nickel (Ni) in batteries (e.g., nickel-metal hydride battery (NiMH), lithium nickel cobalt aluminum oxide (NCA) and lithium nickel manganese cobalt oxide (NMC)) aim to ensure higher energy density and greater storage capacity. Two typical layered nickel-rich ternary cathode materials, NCA and NMC, are commercialized as advanced lithium-ion batteries (LiBs) for electric vehicles (EVs). The technology of those batteries has been improving by steadily increasing the nickel content in each cathode generation. In this study, we consider two types of batteries having a composite cathode made of Li [Ni0.80Co0.1Al0.1]O2, and Li [Ni0.33Mn0.33Co0.33]O2, which are the most common cathode materials for LiBs in EVs since 2010 and their functional recycling is performed. The increasing use of nickel in battery technologies has resulted in the continuous growth of demand for nickel over recent years. Nickel was added to the list of critical materials by the United States Geological Survey (USGS) already in 2021. Unfortunately now, the sustainable supply of nickel is even at higher risk due to the sanctions-related disruption of supplies from Russia. Therefore, enhancing the circularity of nickel starts to be vital for many economies. Demand for recycled nickel is growing, however, a systematic analysis of the sustainability of its recycling is still missing. Therefore, we provide a comprehensive assessment of the sustainability of the global primary and secondary production of nickel. Using system dynamics modelling integrated with geometallurgy principles and by analyzing the processing routes (pyrometallurgical and hydrometallurgical processes), we quantify the key environmental concerns across the life cycle of primary and secondary nickel required for sustainable mobility transition. Energy consumption, water use, and related emissions are assessed for all stages of the nickel supply chain, from mining to recycling. Our analysis shows the possibility of reducing the emissions by around 4.7 mt for GHG, 6.9 kt for PM2.5, 34.3 t for BC, 2.8 kt for CH4, 7.5 kt for CO, 3.3 mt for CO2, 169.9 t for N2O, 3.8 kt for NOx, 11.8 kt for PM10, 104.8 t for POC, 1.6 mt for SOx, and 232.5 t for VOC by engaging in the secondary production of nickel through the recycling of batteries. However, identical growth rate of energy consumption and water use compared to nickel mass flows means no technical progress has been achieved in different stages of the nickel supply chain towards sustainability over the period 2010–2030. Therefore, an improvement in technology is needed to save energy and water in nickel production processes. The results and findings of this study contribute to a better understanding of the necessity for improving closed-loop supply chain policies for nickel.
Steam cracking of naphtha is an important process for the production of olefins. Applying artificial intelligence helps achieve high-frequency real-time optimization strategy and process control. This work employs an artificial neural network (ANN) model with two sub-networks to simulate the naphtha steam cracking process. In the first feedstock composition ANN, the detailed feedstock compositions are determined from the limited naphtha bulk properties. In the second reactor ANN, the cracking product yields are predicted from the feedstock compositions and operating conditions. The combination of these two sub-networks has the ability to accurately and rapidly predict the product yields directly from naphtha bulk properties. Two different feedstock composition ANN strategies are proposed and compared. The results show that with the special design of dividing the output layer into five groups of PIONA, the prediction accuracy of product yields is significantly improved. The mean absolute error of 11 cracking products is 0.53wt% for 472 test sets. The comparison results show that this indirect feedstock composition ANN has lower product prediction errors, not just the reduction of the total error of the feedstock composition. The critical factor is ensuring that PIONA contents are equal to the actual values. The use of an indirect feedstock composition strategy is a means that can effectively improve the prediction accuracy of the whole ANN model.
Despite the success of multiscale modeling in science and engineering, embedding molecular-level information into nonlinear reactor design and control optimization problems remains challenging. In this work, we propose a computationally tractable scale-bridging approach that incorporates information from multi-product microkinetic (MK) models with thousands of rates and chemical species into nonlinear reactor design optimization problems. We demonstrate reduced-order kinetic (ROK) modeling approaches for catalytic oligomerization in shale gas processing. We assemble a library of six candidate ROK models based on literature and MK model structure. We find that three metrics—quality of fit (e.g., mean squared logarithmic error), thermodynamic consistency (e.g., low conversion of exothermic reactions at high temperatures), and model identifiability—are all necessary to train and select ROK models. The ROK models that closely mimic the structure of the MK model offer the best compromise to emulate the product distribution. Using the four best ROK models, we optimize the temperature profiles in staged reactors to maximize conversions to heavier oligomerization products. The optimal temperature starts at 630–900K and monotonically decreases to approximately 560 K in the final stage, depending on the choice of ROK model. For all models, staging increases heavier olefin production by 2.5% and there is minimal benefit to more than four stages. The choice of ROK model, i.e., model-form uncertainty, results in a 22% difference in the objective function, which is twice the impact of parametric uncertainty; we demonstrate sequential eigendecomposition of the Fisher information matrix to identify and fix sloppy model parameters, which allows for more reliable estimation of the covariance of the identifiable calibrated model parameters. First-order uncertainty propagation determines this parametric uncertainty induces less than a 10% variability in the reactor optimization objective function. This result highlights the importance of quantifying model-form uncertainty, in addition to parametric uncertainty, in multi-scale reactor and process design and optimization. Moreover, the fast dynamic optimization solution times suggest the ROK strategy is suitable for incorporating molecular information in sequential modular or equation-oriented process simulation and optimization frameworks.