Dairy wastewater processing and automatic control for waste recovery at the municipal wastewater treatment plant based on modelling investigations

Bacterial networks and enzyme genes in bacterial floccules from hydrolysis and aeration reactors in a dairy wastewater treatment system

The dairy industry generates substantial wastewater, which is commonly treated using integrated anaerobic hydrolysis and aerated biofilm reactors. However, the bacterial composition and functional differences within the generated floccules remain unclear. In this study, we employed 16S rRNA and metagenomic sequencing to compare bacterial communities and enzyme gene profiles between suspended floccules from the hydrolysis ponds and the aeration ponds. Results revealed that the bacterial phyla Firmicutes, Proteobacteria, and Bacteroidetes dominated the wastewater treatment system and the relative abundance of these bacterial phyla varied in each pond. Additionally, the aeration ponds exhibited higher bacterial operational taxonomic units and enzyme gene abundance. Network analysis demonstrated a more complex bacterial network structure in the hydrolysis ponds compared to the aeration ponds. Furthermore, enzyme gene abundance revealed higher metabolic enzyme genes in the hydrolysis ponds, while signal transduction enzyme genes were more abundant in the aeration ponds. Notably, the top 10 bacterial genera, primarily Hydromonas in the hydrolysis ponds and Ferruginibacter in the aeration ponds, exhibited distinct contributions to signal transduction enzyme genes. Hydromonas dominated the metabolic enzyme genes in both ponds. These findings provide crucial insights for optimizing dairy wastewater treatment technologies.

Intelligent Measurement and Analysis of Sewage Treatment Parameters based on Fuzzy Neural Algorithm with ARM9 Core CPU

After entering the new century, the state continues to increase the construction of urban sewage treatment projects in response to the deteriorating water pollution situation. How to collect and analyze the sewage parameter variables in the sewage treatment process to ensure the intelligent measurement and accurate operation of the parameters in the treatment application is an urgent problem to be solved. This paper is mainly based on the computer-aided control system built by the ARM9 core embedded chip, and the feasibility and effectiveness of fuzzy neural network algorithms are discussed to improve the intelligent processing of sewage treatment parameters. After analyzing the principle and implementation flow of fuzzy control and neural network, starting from the characteristics of data collected by ARM9 core chip, the hybrid algorithm model is optimized and improved to further improve the convergence speed and accuracy of the algorithm. The simulation experiment proves that the optimized fuzzy neural control algorithm can effectively identify the dissolved oxygen, nitrate nitrogen, and other parameter data in the sewage treatment, and the recognition accuracy is very close to the true precision. Based on biosensors, the ARM9 core chip control system established by a recursive fuzzy neural network can greatly improve the tracking and control ability of parameters such as dissolved oxygen concentration and nitrate nitrogen concentration in microbial degradation. This has a good development prospect in wastewater treatment control applications. The experimental results show that the recursive fuzzy neural network algorithm proposed in this paper can dynamically track and control the nitrate concentration and dissolved oxygen concentration and ensure that the control is within the accuracy range. The accuracy of recognition is very close to the real accuracy.

Deployment and Optimisation of a Pilot-Scale IASBR System for Treatment of Dairy Processing Wastewater

Increased pressure is being applied to industrial wastewater treatment facilities to adhere to more stringent regulations for the discharge of treated wastewater and to improve energy efficiency of the process. Nitrogen and phosphorous removal can be challenging to achieve efficiently, and in the case of phosphorous removal, can often necessitate the use of chemicals. There is a major drive globally to improve wastewater treatment infrastructure, whilst simultaneously reducing the carbon footprint of the process. The intermittently aerated sequencing batch reactor offers a modification of the well-known sequencing batch reactor process that can enable lower energy requirements than conventional sequencing batch reactor processes and can facilitate enhanced nutrient removal capacities. However, to date much of the previous literature has focused on relatively short laboratory-scale trials (often with synthetic wastewater) which may not be representative of larger scale system performance. This study explored the intermittently aerated sequencing batch reactor technology via a case-study deployment at a dairy production facility, in terms of treatment efficiency and energy efficiency with a focus on optimisation between phases. High treatment capacity and operational flexibility was achieved with NH4-N removals averaging >89%, PO4-P removal averaging >90% and total suspended solids removal averaging >97%. This research demonstrates the characteristics of intermittently aerated sequencing batch reactor technology at scale to effectively achieve biological nutrient removal. In addition, this study demonstrated that when effectively managed, energy savings and reductions in carbon emissions in the region of 36–68% are achievable through optimisation of reactor operation.