The appraisal of anaerobic digestion in Ireland to develop improved designs and operational practice

Mesophilic Anaerobic Digestion treating sewage sludge was investigated at five full-scale sewage treatment plants in Ireland. The anaerobic digestion plants are compared and evaluated in terms of design, equipment, operation, monitoring and management. All digesters are cylindrical, gas mixed and heated Continuously Stirred Tank Reactors (CSTR), varying in size from 130m3 to 800m3. Heat exchanger systems heat all digesters. Three plants reported difficulties with the heating systems ranging from blockages to insufficient insulation and design. Exchangers were modified and replaced within one year of operation at two plants. All but one plant had Combined Heat and Power (CHP) systems installed. Parameter monitoring is a problem at all plants mainly due to a lack of staff and knowledge. The plant operators consider pH and temperature the most important parameters to be measured in terms of successful monitoring of an anaerobic digester. The short time taken and the ease at which pH and temperature can be measured may favour these parameters. Three laboratory scale pilot anaerobic digesters were operated using a variety of feeds over at 144-day period. Two of the pilots were unmixed and the third was mechanically mixed. As expected the unmixed reactors removed more COD by retention of solids in the digesters but also produced greater quantities of biogas than the mixed digester, especially when low solids feed such as whey was used. The mixed digester broke down more solids due to the superior contact between the substrate and the biomass. All three reactors showed good performance results for whey and sewage solids. Scum formation occurred giving operational problems for mixed and unmixed reactors when cattle slurry was used as the main feed source. The pilot test was also used to investigate which parameters were the best indicators of process instability. These trials clearly indicated that total Volatile Fatty Acid (VFA) concentrations was the best parameter to show signs of early process imbalance, while methane composition in the biogas was good to indicate possible nutrient deficiencies in the feed and oxygen shocks. pH was found to be a good process parameter only if the wastewater being treated produced low bicarbonate alkalinities during treatment.

Application of multivariate statistical process control to fuel cell manufacturing

Univariate statistical control charts, such as the Shewhart chart, do not satisfy the requirements for process monitoring on a high volume automated fuel cell manufacturing line. This is because of the number of variables that require monitoring. The risk of elevated false alarms, due to the nature of the process being high volume, can present problems if univariate methods are used. Multivariate statistical methods are discussed as an alternative for process monitoring and control. The research presented is conducted on a manufacturing line which evaluates the performance of a fuel cell. It has three stages of production assembly that contribute to the final end product performance. The product performance is assessed by power and energy measurements, taken at various time points throughout the discharge testing of the fuel cell. The literature review performed on these multivariate techniques are evaluated using individual and batch observations. Modern techniques using multivariate control charts on Hotellings T2 are compared to other multivariate methods, such as Principal Components Analysis (PCA). The latter, PCA, was identified as the most suitable method. Control charts such as, scores, T2 and DModX charts, are constructed from the PCA model. Diagnostic procedures, using Contribution plots, for out of control points that are detected using these control charts, are also discussed. These plots enable the investigator to perform root cause analysis. Multivariate batch techniques are compared to individual observations typically seen on continuous processes. Recommendations, for the introduction of multivariate techniques that would be appropriate for most high volume processes, are also covered.