Modelling COD removal in the wastewater treatment of integrated dissolving pulp-kraft pulp and paper mill

The pulp and paper industry requires high water and energy consumption and produces large amounts of wastewater. Due to escalating global concerns on sustainability, wastewater must be processed to meet the environmental standards before being discharged from the plant. This study aims to develop a representative mathematical model as a simulation tool to predict the effluent quality and to determine the strategies for improvement scenarios. The kinetics parameter for microbial activity was determined from pseudo-steady-state operational historical data from Asia Pacific Resources International Limited (APRIL) Kerinci Mill wastewater treatment plant (WWTP). APRIL Kerinci Mill WWTP is designed to remove the suspended solids and organic substances in the wastewater from integrated prehydrolysis kraft dissolving pulp (DP), bleached hardwood kraft pulp (BHKP), and paper mill. This study presents simulation results for modelling using MATLAB Simulink. Steady-state simulation carried out in the model agreed well with average plant operational data and performance. A model simulation was used to predict and assess the effects of longer solids retention time (SRT) on Chemical Oxygen Demand (COD) removal efficiency. Based on the model prediction, longer SRT should be applied to increase COD removal further. Higher influent COD concentration does not have a significant impact on effluent COD as in a complete mix reactor, the influent substrate is immediately diluted to substrate concentration in the reactor. To overcome higher influent flows, biomass concentration in the system must be increased by increasing the recycle ratio. Increasing recycle ratio will impact lower wastage of sludge production and benefit sludge handling costs. The impact on oxygen requirement must also be considered, as aeration costs will be higher. Finding a suitable SRT need to incorporate economic and production efficiency. The model could be improved by considering other operational parameters’ effects and cost reduction initiatives in the future.