Highly efficient CO2 capture with simultaneous iron and CaO recycling for the iron and steel industry

An efficient CO2 capture process has been developed by integrating calcium looping (CaL) and waste recycling technologies into iron and steel production. A key advantage of such a process is that CO2 capture is accompanied by simultaneous iron and CaO recycling from waste steel slag. High-purity CaO-based CO2 sorbents, with CaO content as high as 90 wt%, were prepared easily via acid extraction of steel slag using acetic acid. The steel slag-derived CO2 sorbents exhibited better CO2 reactivity and slower (linear) deactivation than commercial CaO during calcium looping cycles. Importantly, the recycling efficiency of iron from steel slag with an acid extraction is improved significantly due to a simultaneous increase in the recovery of iron-rich materials and the iron content of the materials recovered. High-quality iron ore with iron content of 55.1–70.6% has been recovered from waste slag in this study. Although costing nearly six times as much as naturally derived CaO in the purchase of feedstock, the final cost of the steel slag-derived, CaO-based sorbent developed is compensated by the byproducts recovered, i.e., high-purity CaO, high-quality iron ore, and acetone. This could reduce the cost of the steel slag-derived CO2 sorbent to 57.7 € t−1, appreciably lower than that of the naturally derived CaO. The proposed integrated CO2 capture process using steel slag-derived, CaO-based CO2 sorbents developed appears to be cost-effective and promising for CO2 abatement from the iron and steel industry

Identification of abnormal patterns in AR (1) process using CS-SVM

Using machine learning method to recognize abnormal patterns covers the shortage of traditional control charts for autocorrelation processes, which violate the applicable conditions of the control chart, i.e., the independent identically distributed (IID) assumption. In this study, we propose a recognition model based on support vector machine (SVM) for the AR (1) type of autocorrelation process. For achieving a higher recognition performance, the cuckoo search algorithm (CS) is used to optimize the two hyper-parameters of SVM, namely the penalty parameter c and the radial basis kernel parameter g. By using Monte Carlo simulation methods, the data sets containing samples of eight patters are generated in experiments for verifying the performance of the proposed model. The results of comparison experiments show that the average recognition rate of the proposed model reaches 96.25% as the autocorrelation coefficient is set equal to 0.5. That is apparently higher than those of the SVM model optimized by the particle swarm optimization (PSO) or the genetic algorithm (GA). Another experiment result demonstrates that the average recognition accuracy of the CS-SVM model also reaches higher than 95% for different autocorrelation levels. At last, a lot of data streams in or out of control are simulated to measure the ARL values. The results turn out that the model has an acceptable online performance. Therefore, we believe that the model can be used as a more effective approach for identification of abnormal patterns in autocorrelation process

Further Results on a Curious Arithmetic Function

Let p be an odd prime number and n be a positive integer. Let vpn, N∗, and Q+ denote the p-adic valuation of the integer n, the set of positive integers, and the set of positive rational numbers, respectively. In this paper, we introduce an arithmetic function fp:N∗⟶Q+ defined by fpn≔n/pvpn1−vpn for any positive integer n. We show several interesting arithmetic properties about that function and then use them to establish some curious results involving the p-adic valuation. Some of these results extend Farhi’s results from the case of even prime to that of odd prime

Conformation Variation and Tunable Protein Adsorption through Combination of Poly(acrylic acid) and Antifouling Poly(N-(2-hydroxyethyl) acrylamide) Diblock on a Particle Surface

Adsorption and desorption of proteins on biomaterial surfaces play a critical role in numerous biomedical applications. Spherical diblock polymer brushes (polystyrene with photoiniferter (PSV) as the core) with different block sequence, poly(acrylic acid)-b-poly(N-(2-hydroxyethyl) acrylamide) (PSV@PAA-b-PHEAA) and poly(N-(2-hydroxyethyl) acrylamide)-b-poly(acrylic acid) (PSV@PHEAA-b-PAA) were prepared via surface-initiated photoiniferter-mediated polymerization (SI-PIMP) and confirmed by a series of characterizations including TEM, Fourier transform infrared (FTIR) and elemental analysis. Both diblock polymer brushes show typical pH-dependent properties measured by dynamic light scattering (DLS) and Zeta potential. It is interesting to find out that conformation of PSV@PAA-b-PHEAA uniquely change with pH values, which is due to cooperation of electrostatic repulsion and steric hindrance. High-resolution turbidimetric titration was applied to explore the behavior of bovine serum albumin (BSA) binding to diblock polymer brushes, and the protein adsorption could be tuned by the existence of PHEAA as well as apparent PAA density. These studies laid a theoretical foundation for design of diblock polymer brushes and a possible application in biomedical fields

Biogas dry reforming for syngas production: catalytic performance of nickel supported on waste-derived SiO2

SiO synthesized from photovoltaic waste by a vapor-phase hydrolysis method was applied as a support for a nickel catalyst in a biogas dry reforming process for the first time. The catalytic performance was compared with those of commercial precipitated SiO and ordered mesoporous SiO. Nickel supported on waste-derived SiO exhibited high CH conversion (92.3%) and high CO conversion (95.8%) at 800°C, and there was no deactivation after a 40 h-on-stream test. Catalyst characterization results revealed that the S values and pore properties of catalysts affected the catalytic performance. A higher pore volume/S ratio led to a smaller crystal metal size and higher metal dispersion, thus the catalyst was less prone to deactivation. This discovery will help improve catalyst design. The use of nickel supported on waste-derived SiO, which is competitive with commercial and mesoporous catalysts, shows the use of photovoltaic waste as a high value-added product; it can also deliver a cheap and environmentally benign support for catalysts in the biogas dry reforming process

Synthesis of highly efficient CaO-based, self-stabilizing CO2 sorbents via structure-reforming of steel slag

Capturing anthropogenic CO in a cost-effective and highly efficient manner is one of the most challenging issues faced by scientists today. Herein, we report a novel structure-reforming approach to convert steel slag, a cheap, abundant, and nontoxic calcium-rich industrial waste, as the only feedstock into superior CaO-based, self-stabilizing CO sorbents. The CO capture capacity of all the steel slag-derived sorbents was improved more than 10-fold compared to the raw slag, with the maximum uptake of CO achieving at 0.50 gg. Additionally, the initial steel slag-derived sorbent could retain 0.25 g g, that is, a decay rate of only 12% over 30 carbonation-calcination cycles, the excellent self-stabilizing property allowed it to significantly outperform conventional CaO, and match with most of the existing synthetic CaO-based sorbents. A synergistic effect that facilitated CO capture by CaO-based sorbents was clearly recognized when Mg and Al, the most common elements in steel slag, coexisted with CaO in the forms of MgO and Al, respectively. During the calcium looping process, MgO served as a well spacer to increase the porosity of sorbents together with Al serving as a durable stabilizer to coresist the sintering of CaCO grains at high temperatures