Comparative study between synthetic and dairy wastewaters in single chamber microbial fuel cell for power generation

To investigate the performance of microbial fuel cell (MFC) with a single-chamber membrane, Pseudomonas aeruginosa is used as a bio catalyst for various synthetic wastewaters rich in carbohydrate and is compared with real dairy wastewater in this experiment. Therefore, the choice of appropriate carbon, nitrogen, NaCl, inoculum content, temperature, and pH process parameters are used for preparing synthetic wastewater was agreed upon by one-variable-at-a time approach. Maximum levels of voltage generation attained from the synthetic wastewater was 485 mV when supple­mented with 1.5 % of lactose as a source of carbon, 0.3 % of ammonium chloride as a decent nitrogen source, 0.03 % of NaCl, inoculum concentration of 3 %, the temperature at 37 oC and pH 7. On the other hand, the maximum voltage attained with real dairy wastewater was 561 mV with high chemical oxygen demand (COD) value of 801 mg l-1. The maximum power density obtained from dairy wastewater was 73.54 mW m-2. Thus, High voltage achieved for MFC operating with real dairy wastewater suggests that it can be used not only for the industrial application to generate more renewable power, but also for the wastewater treatment carried out at the same time.

LSTM and GRU Neural Networks as Models of Dynamical Processes Used in Predictive Control: A Comparison of Models Developed for Two Chemical Reactors

This work thoroughly compares the efficiency of Long Short-Term Memory Networks (LSTMs) and Gated Recurrent Unit (GRU) neural networks as models of the dynamical processes used in Model Predictive Control (MPC). Two simulated industrial processes were considered: a polymerisation reactor and a neutralisation (pH) process. First, MPC prediction equations for both types of models were derived. Next, the efficiency of the LSTM and GRU models was compared for a number of model configurations. The influence of the order of dynamics and the number of neurons on the model accuracy was analysed. Finally, the efficiency of the considered models when used in MPC was assessed. The influence of the model structure on different control quality indicators and the calculation time was discussed. It was found that the GRU network, although it had a lower number of parameters than the LSTM one, may be successfully used in MPC without any significant deterioration of control quality.

Real-time implementation of QFT, GA, and BFTPSO controller for pH neutralization system

The control of a pH process is complex because of severe nonlinearities in its behavior. A continuous pH neutralization process is usually represented as a first-order plus dead time system, but its gain varies for different operating points. Therefore, a conventional linear controller cannot be used, and the pH system was thus represented as a linear state-space model around an equilibrium point. This linear model was then used to compute the PID controller gains using robust and optimization techniques like quantitative feedback theory, bacterial foraging technique-based particle swarm optimization algorithm, and genetic algorithm. The corresponding controller gains resulting from the three algorithms were used to control the pH using a reconfigurable I/O device, NI myRIO-1900. Finally, the output time domain specifications and the servo and regulatory responses, resulting from the three algorithms, were compared in simulation and in real-time to deduce the appropriate tuning algorithm for this system.

Wood-Industrial Waste Material as a Potential Sorbent for the Removal of Pb+2 and Co+2 from Mono and Binary Aquatic Metal Solutions

In this research, the practical feasibility of sawdust waste products from wood-processing industries was evaluated for the elimination of Pb+2 and Co+2 metal ions from mono and binary aquatic solutions. The batch method was used to achieve optimum conditions of including the amount of sorbent, pH, process time, and concentration of metal ions. The absorptive cycle reported maximum removal of lead and cobalt within pH range 6.0 at an initial concentration of 10 mg L–1. Kinetics data collected during the adsorption of both metals is better represented in a pseudo-second-order layout. The equilibrium of adsorption is based on the concept of Langmuir adsorption layout. Thermodynamic parameters demonstrated the feasibility, spontaneity, and endothermic character of heavy metal sorption. The sorption of metal ions was verified by instrumental experiments for example scanning electron microscope (SEM), energy dispersive x-ray spectrometry (EDX), and Fourier transform infrared spectroscopy (FTIR). Thus, sawdust can be an effective material for removing Pb+2 and Co+2 ions from aquatic solutions.

Removal of aqueous carbamazepine using graphene oxide nanoplatelets: process modelling and optimization

Unplanned and unmonitored developmental activities have resulted in a rapid emergence of pollutants like pharmaceuticals and personal care products (PPCPs) in the environment. These PPCPs are considered as potential health hazards. A wide variety of physical, biological and chemical processes are presently being investigated for ensuring the efficient removal of such pollutants from effluents. The present study investigates the potential of graphene oxide nanoplatelets (GONPs) for removal of a common and extensively used drug, Carbamazepine (CBZ) from aqueous solutions. Batch studies were performed to assess the potential of graphene oxide for adsorption of CBZ under different conditions of initial CBZ concentration, adsorbent dosage, temperature and solution pH. Process optimization was performed using Response Surface Methodology and Artificial Neural Network modelling. Results obtained indicated 99% CBZ removal under optimum solution pH, adsorbent dosage and treatment duration of 6, 1 g L− 1 and 120 min respectively. Results revealed that CBZ adsorption by GONPs followed Temkin isotherm and pseudo second order kinetics. A subsequent reusability study established that the GONPs could be reused for up to 8 times without any loss of adsorption efficiency. Therefore, it can be concluded that graphene oxide reported herein has immense potential for adsorption of trace organic pollutants from aqueous phases.