Adaptive regulation of a biogas tower reactor

A simple adaptive high-gain regulator is designed for a nonlinear multivariable Biogas Tower Reactor. The controller achieves tracking of the constant reference signals within a prespecified lambda-neighbourhood within a prespecified time T. The adaptation strategy is very robust and tolerates large disturbances. The results have been tested on an industrial pilot reactor of almost full scale plant

Adaptive and non-adaptive control without identification: a survey

Three related but distinct scenarios for tracking control of uncertain systems are reviewed: asymptotic tracking, approximate tracking with prescribed asymptotic error bound, tracking with prescribed transient behaviour. A variety of system classes are considered, ranging from finite-dimensional linear minimum-phase systems to nonlinear, infinite-dimensional systems described by functional differential equations. These classes are determined only by structural assumptions, such as stable zero dynamics and known relative degree. The objective is a single (and simple) control structure which is effective for every member of the underlying system class: no attempt is made to identify the particular system being controlled

Adaptive λ-[lambda]-tracking control of activated sludge processes

An adaptive controller for activated sludge processes is introduced. The control objective is to keep, in the presence of input constraints, the concentration of the biomass proportional to the influent flow rate, where a prespecified small tracking error of size lambda is tolerated. This is achieved by the so called lambda-tracker which is simple in its design, relies only on structural properties of the process and weak feasibility properties, and does not invoke any estimation or identification mechanism or probing signals. lambda-Tracking is proved for a model of an activated sludge process with unknown reaction kinetics and including unknown time-varying process parameters. It is illustrated by simulations that the lambda-tracker works successfully, and even under practical circumstances which go beyond what we can prove mathematically, it can cope with 'white noise' corrupting the measurement and periodically acting disturbances

Soft sensor development and process control of anaerobic digestion

This thesis focuses on soft sensor development based on fuzzy logic used for real time online monitoring of anaerobic digestion to improve methane output and for robust fermentation. Important process parameter indicators such as pH, biogas production, daily difference in pH and daily difference in biogas production were used to infer alkalinity, a reliable indicator of process stability. Additionally, a fuzzy logic and a rule-based controller were developed and tested with single stage anaerobic digesters operating with cow slurry and cellulose. Alkalinity predictions from the fuzzy logic algorithm were used by both controllers to regulate the organic loading rate that aimed to optimise the biogas process. The predictive performance of a software sensor determining alkalinity that was designed using fuzzy logic and subtractive clustering and was validated against multiple linear regression models that were developed (Partner N° 2, Rothamsted Research 2010) for the same purpose. More accurate alkalinity predictions were achieved by utilizing a fuzzy software sensor designed with less amount of data compared to a multiple linear regression model whose design was based on a larger database. Those models were utilised to control the organic loading rate of a twostage, semi-continuously fed stirred reactor system. Three 5l reactors without support media and three 5l reactors with different support media (burst cell reticulated polyurethane foam coarse, burst cell reticulated polyurethane foam medium and sponge) were operated with cow slurry for a period of seven weeks and twenty weeks respectively. Reactors with support media were proven to be more stable than the reactors without support media but did not exhibit higher gas productivity. Biomass support media were found to influence digester recovery positively by reducing the recovery period. Optimum process parameter ranges were identified for reactors with and without support media. Increased biogas production was found to occur when the loading rates were 3-3.5g VS/l/d and 4-5g VS/l/d respectively. Optimum pH ranges were identified between 7.1-7.3 and 6.9-7.2 for reactors with and without support media respectively, whereas all reactors became unstable at ph<6.9. Alkalinity levels for system stability appeared to be above 3500 mg/l of HCO3 - for reactors without media and 3480 mg/l of HCO3 - for reactors with support media. Biogas production was maximized when alkalinity was 3 between 3500-4500 mg/l of HCO3 - for reactors without support media and 3480- 4300 mg/l of HCO3 - for reactors with support media. Two fuzzy logic models predicting alkalinity based on the operation of the three 5l reactors with support media were developed (FIS I, FIS II). The FIS II design was based on a larger database than FIS I. FIS II performance when applied to the reactor where sponge was used as the support media was characterized by quite good MAE and bias values of 466.53 mg/l of HCO3- and an acceptable value for R2= 0.498. The NMSE was close to 0 with a value of 0.03 and a slightly higher FB= 0.154 than desired. The fuzzy system robustness was tested by adding NaHCO3 to the reactor with the burst cell reticulated polyurethane foam medium and by diluting the reactor where sponge was used as the support media with water. FIS I and FIS II were able to follow the system output closely in the first case, but not in the second. FIS II functionality as an alkalinity predictor was tested through the application on a 28l cylindrical reactor with sponge as the biomass support media treating cow manure. If data that was recorded when severe temperature fluctuations occurred (that highly impact digester performance), are excluded, FIS II performance can be characterized as good by having R2= 0.54 and MAE=Bias= 587 mg/l of HCO3-. Predicted alkalinity values followed observed alkalinity values closely during the days that followed NaHCO3 addition and water dilution. In a second experiment a rulebased and a Mamdani fuzzy logic controller were developed to regulate the organic loading rate based on alkalinity predictions from FIS II. They were tested through the operation of five 6.5l reactors with biomass support media treating cellulose. The performance indices of MAE=763.57 mg/l of HCO3-, Bias= 398.39 mg/l of HCO3-, R2= 0.38 and IA= 0.73 indicate a pretty good correlation between predicted and observed values. However, although both controllers managed to keep alkalinity within the desired levels suggested for stability (>3480 mg/l of HCO3-), the reactors did not reach a stable state suggesting that different loading rates should be applied for biogas systems treating cellulose.New Generation Biogas (NGB

Robustness of λ-tracking in the gap metric

For m-input, m-output, finite-dimensional, linear systems satisfying the classical assumptions of adaptive control (i.e., (i) minimum phase, (ii) relative degree one and (iii) positive highfrequency gain), it is well known that the adaptive λ-tracker u = −k e, ˙k = max{0, |e|−λ}|e| achieves λ-tracking of the tracking error e if applied to such a system: all states of the closedloop system are bounded and |e| is ultimately bounded by λ, where λ > 0 is prespecified and may be arbitrarily small. Invoking the conceptual framework of nonlinear gap metric, we show that the λ-tracker is robust. In the present setup this means in particular that the λ-tracker copes with bounded input and output disturbances and, more importantly, it may even be applied to a system not satisfying one of the classical conditions (i)-(iii) as long as the initial conditions and the disturbances are small and the system is close (in terms of small gap) to a system satisfying (i)-(iii)

Biomass for Energy Country Specific Show Case Studies

In many domestic and industrial processes, vast percentages of primary energy are produced by the combustion of fossil fuels. Apart from diminishing the source of fossil fuels and the increasing risk of higher costs and energy security, the impact on the environment is worsening continually. Renewables are becoming very popular, but are, at present, more expensive than fossil fuels, especially photovoltaics and hydropower. Biomass is one of the most established and common sources of fuel known to mankind, and has been in continuous use for domestic heating and cooking over the years, especially in poorer communities. The use of biomass to produce electricity is interesting and is gaining ground. There are several ways to produce electricity from biomass. Steam and gas turbine technology is well established but requires temperatures in excess of 250 °C to work effectively. The organic Rankine cycle (ORC), where low-boiling-point organic solutions can be used to tailor the appropriate solution, is particularly successful for relatively low temperature heat sources, such as waste heat from coal, gas and biomass burners. Other relatively recent technologies have become more visible, such as the Stirling engine and thermo-electric generators are particularly useful for small power production. However, the uptake of renewables in general, and biomass in particular, is still considered somewhat risky due to the lack of best practice examples to demonstrate how efficient the technology is today. Hence, the call for this Special Issue, focusing on country files, so that different nations’ experiences can be shared and best practices can be published, is warranted. This is realistic, as it seems that some nations have different attitudes to biomass, perhaps due to resource availability, or the technology needed to utilize biomass. Therefore, I suggest that we go forward with this theme, and encourage scientists and engineers who are researching in this field to present case studies related to different countries. I certainly have one case study for the UK to present

Sewage Treatment Plants

Sewage Treatment Plants: Economic Evaluation of Innovative Technologies for Energy Efficiency aims to show how cost saving can be achieved in sewage treatment plants through implementation of novel, energy efficient technologies or modification of the conventional, energy demanding treatment facilities towards the concept of energy streamlining. The book brings together knowledge from Engineering, Economics, Utility Management and Practice and helps to provide a better understanding of the real economic value with methodologies and practices about innovative energy technologies and policies in sewage treatment plants