The pulp and paper industry is one of the most energy-intensive industries worldwide. An energy audit is a primary step toward improving energy efficiency at the facility level. This paper describes a plant-wide energy audit aimed at identifying energy conservation and carbon dioxide (CO 2 ) mitigation opportunities at a paper mill in Guangdong province, China. We describe the energy audit methods, relevant Chinese standards, methods of calculating energy and carbon indicators, baseline energy consumption and CO 2 emissions of the audited paper mill, and nine energy-efficiency improvement opportunities identified by the audit. For each of the nine options, we evaluate the energy conservation and associated CO 2 mitigation potential. The total technical energy conservation potential for these nine opportunities is 967.8 terajoules (TJ), and the total CO 2 mitigation potential is equal to 93,453 tonnes CO 2 annually, representing 14.4 percent and 14.7 percent, respectively, of the mill's total energy consumption and CO 2 emissions during the audit period.

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The gradually depleting fossil resources and the biosafety of bisphenol A have always restricted the green development of the traditional epoxy resin field. In this Article, biomass macromolecule lignin sulfonates are selected as the raw material instead of traditional bisphenol A to prepare lignin-based epoxy resin adhesives. Lignin sulfonates are chemically modified and combined with a cross-linking agent to form lignin-based epoxy resin adhesives with double-interpenetrating-network structures. The resulting lignin-based epoxy adhesive exhibits a maximum tensile shear strength of 11.29 MPa, which is 213% higher than the strength before chemical modification. The tensile shear strength of the adhesive is still 10.13 MPa after 12 h of immersion in water (20 °C), and its tensile shear strength is 9.30 MPa after 12 h of immersion in boiling water (100 °C). The high-temperature and high-humidity environment has no significant effect on the properties of the resulting lignin-based epoxy adhesive.

An influent COD prediction model based on the CNN-LSTM deep learning algorithm is proposed as the basis of aeration control in WWTPs.

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