As a multifunctional material, biochar is considered a potential adsorbent for removing heavy metals from wastewater. Most biochars with high adsorption capacities have been modified, but this modification is uneconomical, and modifying biochar may cause secondary pollution. Thus, it is necessary to develop an efficient biochar without modification. In this study, spent P. ostreatus substrate and spent shiitake substrate were used as the raw materials to prepare biochar. Then, the physicochemical properties of the biochars and their removal efficiencies for Pb(II) were investigated. The results showed that the physicochemical properties (e.g., large BET surface area, small pore structure and abundant functional groups) contributed to the large adsorption capacity for Pb(II); the maximum adsorption capacities were 326 mg g−1 (spent P. ostreatus substrate-derived biochar) and 398 mg g−1 (spent shiitake substrate-derived biochar), which are 1.6–10 times larger than those of other modified biochars. The Pb(II) adsorption data could be well described by the pseudo-second-order kinetic model and the Langmuir model. This study provides a new method to comprehensively utilize spent mushroom substrates for the sustainable development of the edible mushroom industry.
Painting is the largest energy consumption unit in ship repair enterprises, with annual electrical energy costs amounting to millions of dollars. The energy-saving model of painting business based on artificial intelligence, Internet of Vehicles (IoV) and big data technologies has become a current research hot spot. This paper takes a cargo ship of Youlian Shipyard as an example to improve the quality of special coating construction, reduce cost and increase efficiency. In the process of special coating construction, a comprehensive analysis of equipment usage in different compartments is carried out, and a compartment energy consumption analysis model based on combined weighting and Gray Fuzzy Comprehensive Evaluation is proposed. The model uses factors such as energy consumption, hours, and number of devices as evaluation indicators. Based on the fuzzy comprehensive evaluation, the gray correlation coefficients and comprehensive weights were weighted to obtain the final comprehensive evaluation results of each planning scheme, and then compare them to determine the optimal scheme. The results show that the grey fuzzy comprehensive evaluation model with combined weighting has better results than other models, and the evaluation results are scientific and reasonable. It has certain application value in multi-objective scheme optimization.
In seismic exploration, matching pursuit (MP), which decomposes wavelets based on best signal matching, can not only extract accurate frequency information, but also can pick up the strong amplitudes of possible bright spots adaptively according to the threshold or iteration control. RGB (Red-Green-Blue) blending technique of spectral bands can make full use of the information of all frequency bands, which can reflect the general frequency changes of seismic data reducing the interpretation ambiguity. Therefore, in order to improve the precision of interpretation based on bright spot technique, the authors take both amplitude and frequency into account and propose a new approach for hydrocarbon identification by combining both methods. First, we choose the decomposed wavelets of strong amplitudes related to hydrocarbons in MP algorithm to predict the possible bright spots, so as to remove the effect of unrelated reflections in the bright spots interpretation. Then, RGB blending technique is used to the low-, mid-, and high-frequency spectral bands of the bright spot prediction section to assist for the hydrocarbon identification. Finally, the validity of the method is verified by both the model and field data tests. Results demonstrate that the proposed method can improve the precision of hydrocarbon identification, and reduce the interpretation ambiguity of the reservoirs.
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