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

Evaluating the impact of a polypharmacy Action Learning Sets tool on healthcare practitioners’ confidence, perceptions and experiences of stopping inappropriate medicines

Background: issues of medication adherence, multimorbidity, increased hospitalisation risk and negative impact upon quality of life have led to the management of polypharmacy becoming a national priority. Clinical guidelines advise a patient-centred approach, involving shared decision-making and multidisciplinary team working. However, there have been limited educational initiatives to improve healthcare practitioners’ management of polypharmacy and stopping inappropriate medicines. This study aimed to evaluate the impact of a polypharmacy Action Learning Sets (ALS) tool across five areas: i. healthcare practitioners’ confidence and perceptions of stopping medicines; ii. knowledge and information sources around stopping medicines; iii. perception of patients and stopping medicines; iv. perception of colleagues and stopping medicines and v. perception of the role of institutional factors in stopping medicines. Methods: the ALS tool was delivered to a multi-disciplinary group of healthcare practitioners: GPs [n = 24] and pharmacy professionals [n = 9]. A pre-post survey with 28 closed statements across five domains relating to the study aims [n = 32] and a post evaluation feedback survey with 4 open-ended questions [n = 33] were completed. Paired pre-post ALS responses [n = 32] were analysed using the Wilcoxon signed-rank test. Qualitative responses were analysed using a simplified version of the constant comparative method. Results: the ALS tool showed significant improvement in 14 of 28 statements in the pre-post survey across the five domains. Qualitative themes (QT) from the post evaluation feedback survey include: i. awareness and management of polypharmacy; ii. opportunity to share experiences; iii. usefulness of ALS as a learning tool and iv. equipping with tools and information. Synthesised themes (ST) from analysis of pre-post survey data and post evaluation feedback survey data include: i. awareness, confidence and management of inappropriate polypharmacy, ii. equipping with knowledge, information, tools and resources and iii. decision-making and discussion about stopping medicines with colleagues in different settings. Conclusions: this evaluation contributes to developing understanding of the role of educational initiatives in improving inappropriate polypharmacy, demonstrating the effectiveness of the ALS tool in improving healthcare practitioners’ awareness, confidence and perceptions in stopping inappropriate medicines. Further evaluation is required to examine impact of the ALS tool in different localities as well as longer-term impact.</p

Independent evaluation of the piloting of the implementation of MymHealth [myCOPD, myHeart and myDiabetes] by Dorset Clinical Commissioning Group

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Identifying on admission patients likely to develop acute kidney injury in hospital

BackgroundThe incidence of Acute Kidney Injury (AKI) continues to increase in the UK, with associated mortality rates remaining significant. Approximately one fifth of hospital admissions are associated with AKI and approximately a third of patients with AKI in hospital develop AKI during their time in hospital. A fifth of these cases are considered avoidable. Early risk detection remains key to decreasing AKI in hospitals, where sub-optimal care was noted for half of patients who developed AKI.MethodsElectronic anonymised data for adults admitted into the Royal Cornwall Hospitals Trust (RCHT) between 18th March and 31st December 2015 was trimmed to that collected within the first 24 h of hospitalisation. These datasets were split according to three separate time periods: data used for training the Takagi-Sugeno Fuzzy Logic Systems (FLS) and the multivariable logistic regression (MLR) models; data used for testing; and data from a later patient spell used for validation.Three fuzzy logic models and three MLR models were developed to link characteristics of patients diagnosed with a maximum stage AKI within 7 days of admission: the first models to identify any AKI Stage (FLS I, MLR I), the second for patterns of AKI Stage 2 or 3 (FLS II, MLR II), and the third to identify AKI Stage 3 (FLS III, MLR III). Model accuracy is expressed by area under the curve (AUC).ResultsAccuracy for each model during internal validation was: FLS I and MLR I (AUC 0.70, 95% CI: 0.64–0.77); FLS II (AUC 0.77, 95% CI: 0.69–0.85) and MLR II (AUC 0.74, 95% CI: 0.65–0.83); FLS III and MLR III (AUC 0.95, 95% CI: 0.92–0.98).ConclusionsFLS II and FLS III (and the respective MLR models) can identify with a high level of accuracy patients at high risk of developing AKI in hospital. These two models cannot be properly assessed against prior studies as this is the first attempt at quantifying the risk of developing specific Stages of AKI for a broad cohort of both medical and surgical inpatients. FLS I and MLR I performance is comparable to other existing models