M. Poch

Energy Saving in a Wastewater Treatment Process: an Application of Fuzzy Logic Control

Many uncertain factors affect the operation of Wastewater Treatment Plants. Due to the complexity of biological wastewater treatment processes, classical methods show significant difficulties when trying to control them automatically. Consequently soft computing techniques and, specifically, fuzzy logic appears to be a good candidate for controlling these ill-defined, time-varying and non-linear systems. This paper describes the development and implementation of a Fuzzy Logic Controller to regulate the aeration in the Taradell Wastewater Treatment Plant. The main goal of this control process is to save energy without decreasing the quality of the effluent discharged. The fuzzy controller integrates the information coming from two different signals: the Dissolved Oxygen and Oxidation-Reduction Potential values. The simulation results proved that fuzzy logic is a good tool for controlling the aeration of the wastewater treatment plant. The results obtained show that energy savings of more than 10% can be achieved using aeration fuzzy control and at the same time still keeping the good removal levels.

Development of a real-time expert system for wastewater treatment plants control

Abstract A real-time expert system to control wastewater treatment plants is presented. The software has been developed in the G2 environment. It contains: an interface that permits on-line acquisition of plant data using G2 standard interface (GSI), a predictive control algorithm for dissolved oxygen (DO) control, and a graphical interface between the expert system and the operator. The dissolved oxygen control is performed using a non-linear predictive control algorithm, that has been developed to satisfy quality constraints whilst reducing energy demands. The algorithm uses data obtained from the plant by hardware sensors, and software which recursively estimates the oxygen uptake rate. All these elements are integrated in a knowledge base that includes a set of diagnosis, detection, prediction and operation rules, making the system capable of handling a wide number of usual (where predictive control can be useful) and unusual situations (where quantitative and qualitative information must be considered.

Development of a case-based system for the supervision of an activated sludge process.

The development of a case-based reasoning system for the supervision of an activated sludge process is presented here. The methodology proposed permits the use of past experiences to solve new problems that arise in the process. These experiences are classified as cases or situations. The adaptation of cases and the generation of new cases are used to tune the response of the system and to learn from the new information generated by the process. The case and the case library definition, the initial seed, the search and retrieval process, the adaptation, the action, the evaluation and the learning steps are presented and outlined. The process studied is the wastewater treatment plant of Girona, Spain. Two examples of the response of the system to two different operational situations are presented. The paper also outlines the integration of different fields in a multidisciplinary approach as the most optimal solution to ensure the successful control and supervision of complex processes like the activated sludge process. With this aim the integration of an array of specific supervisory intelligent systems (for the logical analysis and reasoning) and numerical computations for detailed engineering is suggested.

Evaluation of natural computation techniques in the modelling and optimization of a sequential injection flow system for colorimetric iron(III) determination

Abstract The present study shows and gives evidence of the applicability of natural computation techniques in the modelling and optimization of a sequential injection flow system of analysis for colorimetric iron(III) determination in water samples. The reaction with thiocyanate is used as reagent colour. A neural network consisting of two hidden layers, each one formed by eight neurons, was used to model the system. Optimization of the system in terms of sensitivity, linearity and sampling rate was carried out by using jointly the neural network and genetic algorithms. The latter were used with a set of 50 crossed and mutated chromosomes over 100 generations. In the system thus developed, 140 μl of sample and 70 μl of reagent were sequentially introduced into the holding coil and propelled toward the detector at a flow of 5 ml/min. The system gave a sampling rate of 110 samples per hour. A comparison of the results obtained in the analysis of six samples with those obtained using the reference method (atomic absorption spectrophotometry) showed the high quality of results provided.

On-line monitoring of lipase production in fermentation processes

The results obtained in on-line monitoring of lipase production byCandida rugosa in a batch fermentation process are presented. For this purpose an automatic lipase concentration analyzer has been developed and tested. Its good reproducibility and its capability to perform the analysis in less time than the classical titrimetric method make it very suitable for on-line determination lipase concentration in fermentation processes.

Mathematical modelling of sequential determinations by flow-injection sandwich techniques

Abstract Preliminary results obtained in the evaluation of a mathematical model for flow-injection systems, including chemical kinetics, with an eight-way injection valve are presented. The use of this kind of injection valve permits the insertion of the sample bolus between two different reagent solutions (sandwich techniques). The model considers the system as a tubular reactor with axially dispersed plug flow. As an example for systems with chemical reaction, the enzymatic determination of glucose was chosen. The parameters of the model (dispersion coefficients and reaction rate constants) were experimentally evaluated by using a tracer or by unidirectional optimization, respectively. The effect of different parameters of the flow system on the analytical signal for one analyte and one reagent was simulated, and the model results are compared with experimental data obtained under the same conditions.

Evaluation of microwave digestion for chemical oxygen demand determination

Abstract A microwave digestion procedure has been evaluated for use in the determination of chemical oxygen demand (COD) by the dichromate method, using small volumes of sample and reagents. Different organic substances with COD values between 50 and 500 mg O2‐1−1 were analyzed by both the proposed procedure and the standard method, obtaining satisfactory results (mean recoveries 91–102%) with digestion times lower than 5 min. Samples of river water and wastewater were also analyzed, obtaining results showing good agreement with the standard method.

Comparison of the Powell and simplex methods in the optimization of flow-injection systems. Simulation on modelled experimental surfaces and experimental optimizations

Abstract The Powell algorithm is adapted for the optimization of a flow-injection system for the spectrophotometric determination of ammonia based on the indophenol blue reaction. Its performance is compared with that of the modified simplex method in experimental optimization, and in simulation of the course along the experimental response surface, which can be modelled conveniently for this analytical system. The Powell algorithm needed fewer evaluations of the objective function (maximal sensitivity and sample throughput), thus minimizing experimental work, particularly in the initial optimization.

Biomass estimation using on-line glucose monitoring by flow injection analysis : application to Candida rugosa batch growth

AbstructA new Flow Injection Analysis (FIA) system was developed that permits the on-line monitoring of glucose. This information may be used to identify the biomass concentration. The proposed manifold allows the automatic analysis of glucose from 0 to 90 g/L. The results are in agreement with the analyses provided by HPLC. The system was applied to on-line monitoring of substrate in severalCandida rugosa batches grow without detecting mechanical or biochemical problems. This information was linked to a Extended Kaiman Filter (EKF) recursive estimation scheme to identify the vector state of the system.Index Entries: Flow Injection Analysis; on-line glucose analysis; biomass identification; Candida rugosa.

An integrated design strategy for flow-injection analysis based on the coupling of mathematical modelling and optimization algorithms

A methodology to design a flow- injection system with optimized performance characteristics for different experimental requirements is presented which requires minimal experimental effort. Departing from basic experimental knowledge of the hydrodynamic parameters and rates of the reactions involved, the coupling of a mathematical model describing the flow system with the Powells optimization algorithm allows the automated generation of a complete set of flow- injection configurations, each of them being optimal with respect to its objective function. From this set of configurations, the performance of the flow- injection system is easily visualized, allowing the user to select the system that better suits the requirements of his particular application and exploiting the potentiality of multiple flow- injection optimal configurations.