Petronela Cozma

Modeling and simulation of high pressure water scrubbing technology applied for biogas upgrading

Depending on the end of use, the quality of biogas must be upgraded in order to utilize the maximum amount of energy necessary for proper applications. Upgrading biogas refers to the increase of methane concentration in product gas by removal of CO2, which increases its heating power. Several treatment technologies are available for biogas upgrading: high pressure water scrubbing (HPWS), pressure swing adsorption, membrane separation, chemical absorption, and gas permeation. Water absorption based on the physical effect of dissolving gases in liquids (HPWS) is a well-known technology and the most effective upgrading process, since provides a simultaneous removal of CO2 and H2S. This could ensure an increasing methane concentration and energy content per unit volume of biogas. In spite of this, few studies are published on biogas upgrading using pressurized water technology. In order to elucidate the performance of HPWS technology at industrial scale with the possibility of water regeneration and recirculation, effects of different operating parameters on the removal of undesired components from biogas were examined, based on modeling and simulation tools. For simulation, the commercial software tool Aspen Plus was applied. Equilibrium model was applied for simulating the absorption process. The simulation results were validated with experimental data from the literature. The results are summarized in terms of system efficiency, expressed as CH4 enrichment, methane loss, and CO2 removal. Finally, new data which can be further applied for scale-up calculations and techno-economic analysis of the HPWS process are provided.

Potential of biosorption and bioaccumulation processes for heavy metals removal in bioreactors

Environmental contamination with heavy metals, especially of soils and water, became a significant problem because most of them are toxic to the living organisms, non-degradable and persistent in the contaminated media. Physico-chemical methods, such as chemical precipitation, electrochemical treatment, filtration, ion exchange, evaporation, reverse osmosis and membrane technologies are currently the most used methods to remove heavy metal ions from wastewaters. Bioremediation technologies, which are known to be environmentally sound natural processes, have become attractive alternatives to the conventional methods. Among these, biosorption and bioaccumulation address various interactions and concentration of toxic metals in either living (bioaccumulation) or non-living (biosorption) biomass. The aim of this paper is to emphasize the proficiency of biosorption and bioaccumulation processes applied in different types of bioreactors (stirred tank, fixed bed, fluidized-bed and air-lift) using various microorganisms for the removal of heavy metals from contaminated effluents.

Modelling and Optimization of CO2 Absorption in Pneumatic Contactors Using Artificial Neural Networks Developed with Clonal Selection-Based Algorithm

Abstract Our research focuses on the application of airlift contactors (ALRs) for the decontamination of CO2-containing gas streams, such as biogas. To assess the performance of ALRs during CO2 absorption, a complex experimental programme was applied in a laboratory-scale rectangular pneumatic contactor, able to operate either as a bubble column or as an airlift reactor. Using the experimental data, a model based on artificial neural network (ANN) was developed. The algorithm for determining the optimal neural network model and for reactor optimization is clonal selection (CS), belonging to artificial immune system class, which is a new computational intelligence paradigm based on the principles of the vertebrate immune system. To improve its capabilities and the probability for highly suitable models and input combinations, addressing maximum efficiency, a Back-Propagation (BK) algorithm – a supervised learning method based on the delta rule – is used as a local search procedure. It is applied in a greedy manner for the best antibody found in each generation. Since the highest affinity antibodies are cloned in the next generation, the effect of BK on the suitability of the individuals propagates into a large proportion of the population. In parallel with the BK hybridization of the basic CS–ANN combination, a series of normalization procedures are included for improving the overall results provided by the new algorithm called nCS-MBK (normalized Clonal Selection-Multilayer Perceptron Neural Network and Back-Propagation algorithm). The optimization allowed for achieving the optimal reactor configuration, which leads to a maximum amount of CO2 dissolved in water.

Comparison of Rhodotorula sp. and Bacillus megaterium in the removal of cadmium ions from liquid effluents

Abstract This study compares the capacity of Rhodotorula sp. and Bacillus megaterium for Cd(II) removal considering the influence of operating parameters (pH, biosorbent dosage, contact time, temperature, initial metal concentration in solution). The highest Cd(II) uptake of 14.2 mg/g by Rhodotorula sp. was exhibited at 30°C, when working at pH 6 and with 5 g/l biosorbent dosage, after 48 h of contact time. In these conditions, a removal efficiency of 85% was obtained. Similar outcomes were obtained for B. megaterium (15.1 mg/g, 90%) at 35°C, pH 4 and 3 g/l biosorbent dosage, considered as the optimum set of parameters, equilibrium being achieved for a contact time of 20 min. The possible interaction mechanisms between the biosorbents and Cd(II) were evaluated through point of zero charge (pHpzc), Fourier transform infrared (FTIR), spectroscopy and scanning electron microscopy coupled with energy dispersive X-ray microanalysis (SEM-EDX). Data were modeled using pseudo-first and pseudo-second order kinetic models and Langmuir and Freundlich isotherms models. Further studies considered a modeling approach based on linear regression with Durbin-Watson statistics, while the accuracy and precision of experiments were evaluated by ANOVA.

Life cycle assessment of paper manufacturing: Environmental and human health impacts

The reduction of production waste impact started to be an important subject in the context of industrial development. In the last years the authorities became to be interested in the reduction of impacts generated by the large amount of waste, by recycling or reuse in processes. In this context, the present study is developed to help decision makers to take the best decision regarding the management of production waste from paper manufacturing so as to mitigate its impacts. According to the results obtained after an analysis of the production waste conducted in the Life Cycle Analysis (LCA) framework, the recycling of this type of waste can reduce considerable the impact on the environment and human health by reducing tree cutting and increasing the absorption capacity of CO2 from the environment.

Overview of human health hazards posed by pesticides in plant products

Pesticides use, one of the hottest topics nowadays when it comes to human health, started to be a serious concern to consumers considering their known harmful effects. This paper provides a short overview on pesticides application and their corresponding maximum residue levels (MRLs) in plant products, risks associated to human health and steps in conducting a human health risk assessment. The procedure of assessing the MRLs is discussed and a methodology of assessing human health risks to pesticides is considered based on currently existing framework provided by USEPA. This analysis highlights the requisite of continuous monitoring programmes of pesticide residues in plant products, and deep knowledge of their toxic effects and assessment strategies necessary in human health risk estimation and mitigation.