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The use of microalgae and bacteria as a strategy for the startup of bioreactors for the treatment of industrial wastewater can be a sustainable and economically viable alternative. This technology model provides satisfactory results in the nitrification and denitrification process for nitrogen removal, organic matter removal, biomass growth, sedimentation, and byproducts recovery for added-value product production. The objective of this work was to evaluate the performance of microalgae and bacteria in their symbiotic process when used in the treatment of paper pulp industry wastewater. The experiment, lasting fourteen days, utilized four bioreactors with varying concentrations in mgVSS/L of microalgae to bacteria ratio (R1-100:100, R2-100:300, R3-100:500, R4-300:100) in the startup process. Regarding the sludge volumetric index (SVI), the results show that the R1 and R2 reactors developed SVI30/SVI10 biomass in the range of 85.57 ± 7.33% and 84.72 ± 8.19%, respectively. The lipid content in the biomass of reactors R1, R2, R3 e R4 was 13%, 7%, 19%, and 22%, respectively. This high oil content at the end of the batch, may be related to the nutritional stress that the species underwent during this feeding regime. In terms of chlorophyll, the bioreactor with an initial inoculation of 100:100 showed better symbiotic growth of microalgae and bacteria, allowing exponential growth of microalgae. The total chlorophyll value for this bioreactor was 801.46 ± 196.96 μg/L. Biological removal of nitrogen from wastewater from the paper pulp industry is a challenge due to the characteristics of the effluent, but the four reactors operated in a single batch obtained good nitrogen removal. Ammonia nitrogen removal performances were 91.55 ± 9.99%, 72.13 ± 19.18%, 64.04 ± 21.34%, and 86.15 ± 30.10% in R1, R2, R3, and R4, respectively., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The demand for paper and paper-based packaging has seen a massive increase in past years, resulting in accelerated deforestation to meet the rising demand, negatively impacting the environment, and there is a need to look towards different non-woody raw materials. Kraft pulping (KP) is widely used in paper making, for which the chemical dose, temperature, time, and energy required must be optimized, for which many insignificant experimental trials are performed. An effort is made to solve this problem by developing the regression equations with the help of Excel using One Factor at a Time Analysis (OFAT), followed by carrying out design of experiments (DoE) using orthogonal approach and regression analysis in Minitab software. Life cycle Assessment (LCA) using the Open-LCA software estimates the effect of chemicals and energy required during pulping on human health, ecosystem quality, and resource depletion. Using regression analysis, the equations for predicting kappa number, yield (%), total energy consumed, and mechanical properties of the paper sheet showed a good fit with an R 2 value in the range of 0.90-0.99. Apart from that, the mechanical properties, namely tensile index (41.43 Nm/g), tear index (6.96 mN m 2 /g), bending stiffness (0.5 mN m), and burst index (3.92 kPa m 2 /g) of the unbeaten sheet, were determined experimentally at optimized conditions. Based on the Open-LCA result, the optimized pulping conditions had less impact on human health, ecosystem quality, and resource depletion. Industries can use the model to predict the values of kappa number, yield, mechanical properties, and energy consumption without performing optimization experiments that may impact the industry's economy to a greater extent., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)