Physico-chemical treatment of pulping effluent: Characterization of flocs and sludge generated after treatment

<p>The present study deals with the treatment of simulated pulping effluent (pH = 11, and total organic carbon (TOC) = 1900 mg/L) by acid precipitation and coagulation-flocculation processes. The maximum TOC removal (= 4845 mg/g Al³⁺) from the effluent was found with Al₂(SO₄)₃ coagulant. Addition of sufloc (a flocculant) improved the sludge settling significantly. Hydrolyzed mono- and polynuclear species generated from Fe and Al coagulants could have resulted in TOC removal. Thermal analysis of sludge showed release of gases like CO, CO₂, HCHO, CH₃SH and SO₂ during combustion. Afterwards, the dissolved metal species in treated wastewater samples should be removed.</p

A collaborative reinforcement learning approach to urban traffic control optimization

The high growth rate of vehicles per capita now poses a real challenge to efficient Urban Traffic Control (UTC). An efficient solution to UTC must be adaptive in order to deal with the highly-dynamic nature of urban traffic. In the near future, global positioning systems and vehicle-tovehicle/ infrastructure communication may provide a more detailed local view of the traffic situation that could be employed for better global UTC optimization. In this paper we describe the design of a next-generation UTC system that exploits such local knowledge about a junction’s traffic in order to optimize traffic control. Global UTC optimization is achieved using a local Adaptive Round Robin (ARR) phase switching model optimized using Collaborative Reinforcement Learning (CRL). The design employs an ARR-CRL-based agent controller for each signalized junction that collaborates with neighbouring agents in order to learn appropriate phase timing based on the traffic pattern. We compare our approach to non-adaptive fixed-time UTC system and to a saturation balancing algorithm in a largescale simulation of traffic in Dublin’s inner city centre. We show that the ARR-CRL approach can provide significant improvement resulting in up to ~57% lower average waiting time per vehicle compared to the saturation balancing algorithm