This work presents an experimental and theoretical investigation of anaerobic fluidized bed reactors (AFBRs). The bioreactors are modeled as dynamic three-phase systems. Biochemical transformations are assumed to occur only in the fluidized bed zone. The biofilm process model is coupled to the system hydrodynamic model through the biofilm detachment rate; which is assumed to be a first-order function of the energy dissipation parameter and a second order function of biofilm thickness. Non-active biomass is considered to be particulate material subject to hydrolysis. The model includes the anaerobic conversion for complex substrate degradation and kinetic parameters selected from the literature. The experimental set-up consisted of two mesophilic (36±1ºC) lab-scale AFBRs (R1 and R2) loaded with sand as inert support for biofilm development. The reactor start-up policy was based on gradual increments in the organic loading rate (OLR), over a four month period. Step-type disturbances were applied on the inlet (glucose and acetic acid) substrate concentration (chemical oxygen demand (COD) from 0.85 to 2.66 g L-1) and on the feed flow rate (from 3.2 up to 6.0 L d-1) considering the maximum efficiency as the reactor loading rate switching. The predicted and measured responses of the total and soluble COD, volatile fatty acid (VFA) concentrations, biogas production rate and pH were investigated. Regarding hydrodynamic and fluidization aspects, variations of the bed expansion due to disturbances in the inlet flow rate and the biofilm growth were measured. As rate coefficients for the biofilm detachment model, empirical values of 3.73⋅10(4) and 0.75⋅10(4) s² kg-1 m-1 for R1 and R2, respectively, were estimated.

The present work is focused on evaluating the anaerobic digestion of complex substrates (proteins and carbohydrates) in anaerobic fluidized bed reactors (AFBRs). In a previous paper, an AFBR model was adjusted using only the experimental data obtained during the acclimatization stage (glucose and acetate-based feeding) of two mesophilic bioreactors. Here, the sensitivity of that model to represent the digestion processes of proteins such as gelatin and albumin, and sucrose as a carbohydrate, is analyzed. Some kinetic expressions and parameters for the enzymatic hydrolysis processes need to be modified to represent the real behavior of bioreactors. Under the analyzed operating conditions, a hydraulic retention time higher than 1 day is suggested to ensure good system efficiency.

In this paper, a new procedure for the design and analysis of Heat and Power Plants is presented. In this formulation, thermodynamic solutions obtained by maximizing the plant efficiency are used to find the economic design of the plant. In fact, by applying the Karush-Kuhn-Tucker conditions it is possible to relate thermodynamic and economic solutions. A formal context for the use of thermodynamic models in solving complex optimization problems that arise in the area of synthesis and design of chemical processes is introduced. The proposed methodology has been successfully applied for two plant arrangements. Rigorous models have been used to model the plant equipments. The obtained results are presented in order to illustrate the proposed procedure.

This work presents a computational tool, useful in risk assessment of hazardous material releases. It is based on a methodology that takes into account the stochastic uncertainty of atmospheric parameters. This is relevant for calculating risk of hazardous gases, particulate matter or bioaerosols diffusion when an accidental release or continuous emission occurs. This methodology can be applied to risk analysis of static sources (a stack or a fixed tank in a facility) or transportation accidents (road, train, maritime and pipeline transport) involving different scenarios. After carrying out a stochastic simulation based on well-known diffusion models (heavy and light gases or particulate matter), downwind concentrations are obtained so that individual and societal risks can be computed. This work contributes mainly to the formal presentation of the procedure, and the real application of the stochastic simulation avoiding large (prohibitive) computational effort, by introducing a translation algorithm and the Stochastic Reference Emitter (SRE) definition. This new approach also shows to be a fundamental step to organize a software prototype for risk assessment. Besides, taking advantage of the new computational capability a SRE-based algorithm for safe distances calculation is introduced. Finally, an example shows the main capabilities of the new tool.

A steady state model of an anaerobic methanogenic biofilm reactor-module that accounts for the biological interactions of four microbial groups, ionic equilibrium in solution, gas-liquid transfer phenomena and biofilm processes is presented. The model consists of a continuous stirred tank reactor type that allocates an inert support material, whose specific surface is taken into account. The biofilm model assumes an homogeneous biofilm of uniform thickness and constant density with no mass transfer resistance. The biofilm detachment process rate is modeled as a second-order function on the biofilm thickness and a first-order function on the mass fraction of the fixed biomass concentration of each microbial group. The balance equations for non-active biomass in liquid and biofilm are included. The model predictions have been satisfactorily compared with steady state experimental data reported in literature from a one-phase methanogenic biofilm system treating an acetic acid-based synthetic effluent, and a two-phase system with combined suspended (acidogenic) and attached (methanogenic) microbial growth treating a food industry wastewater composed by two residual process streams.

A rigorous dynamic model of anaerobic biofilm reactor (Mussati et al., 1998) is used to optimize and evaluate different loading strategies in anaerobic wastewater treatment systems. This work includes variations in the original model so that the results of process simulation also represent the initial events of the start up operation as regards the biofilm growth. The model was implemented in gPROMS (General Process Modeling System). Different dynamic optimization formulations are evaluated. Start up strategies are considered and discussed. A sensitivity analysis regarding kinetic data on optimal start up policies under two different conditions is included. The first condition consists of a fixed maximum load, and the other one is the maximum load supported by the system, which varies according to the kinetic data being used. Changes in maximum specific growth rate, half saturation constant and specific death rate of Monod's model generate different optimal start up periods; whereas optimal start up time did not change when the maximum allowed load for each reactor is considered.

In this paper, a new strategy involving an evolutionary algorithmic procedure for the optimization of Multiple Stage Flash (MSF-M) Systems is presented. A "detailed model" of an MSF-M System is developed according to rigorous material, momentum and energy balances for each stage. The model of the MSF-M System is represented as a complex NLP, which incorporates a high number of nonlinear constraints that difficult the global optimum determination. Here we present a hybrid methodology that uses optimal solutions obtained from a "thermodynamic method" to find the "economic global optimal solution". A pre-processing stage (solving successive NLPs) is used to initialize the final NLP problem. A Case Study and a discussion of the results are presented.