Prediction of Batch Processes Runtime Applying Dynamic Time Warping and Survival Analysis

AbstractBatch runs corresponding to the same recipe usually have different duration. The data collected by the sensors that equip batch production lines reflects this fact: time series with different lengths and unsynchronized events. Dynamic Time Warping (DTW) is an algorithm successfully used, in batch monitoring too, to synchronize and map to a standard time axis two series, an action called alignment. The online alignment of running batches, although interesting, gives no information on the remaining time frame of the batch, such as its total runtime, or time-to-end. We notice that this problem is similar to the one addressed by Survival Analysis (SA), a statistical technique of standard use in clinical studies to model time-to-event data. Machine Learning (ML) algorithms adapted to survival data exist, with increased predictive performance with respect to classical formulations. We apply a SA-ML-based system to the problem of predicting the time-to-end of a running batch, and show a new application of DTW. The information returned by openended DTW can be used to select relevant data samples for the SA-ML system, without negatively affecting the predictive performance and decreasing the computational cost with respect to the same SA-ML system that uses all the data available. We tested the system on a real-world dataset coming from a chemical plant

Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset

While perovskite solar cells have reached competitive efficiency values during the last decade, stability issues remain a critical challenge to be addressed for pushing this technology towards commercialisation. In this study, we analyse a large homogeneous dataset of Maximum Power Point Tracking (MPPT) operational ageing data that we collected with a custom-built High-throughput Ageing System in the past 3 years. In total, 2,245 MPPT ageing curves are analysed which were obtained under controlled conditions (continuous illumination, controlled temperature and atmosphere) from devices comprising various lead-halide perovskite absorbers, charge selective layers, contact layers, and architectures. In a high-level statistical analysis, we find a correlation between the maximum reached power conversion efficiency (PCE) and the relative PCE loss observed after 150-hours of ageing, with more efficient cells statistically also showing higher stability. Additionally, using the unsupervised machine learning method self-organising map, we cluster this dataset based on the degradation curve shapes. We find a correlation between the frequency of particular shapes of degradation curves and the maximum reached PCE

The challenge of studying perovskite solar cells’ stability with machine learning

Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technology–which has resulted in the accumulation of a significant amount of data. The best example is the “Perovskite Database Project,” which also includes stability-related metrics. From this database, we use data on 1,800 perovskite solar cells where device stability is reported and use Random Forest to identify and study the most important factors for cell stability. By applying the concept of learning curves, we find that the potential for improving the models’ performance by adding more data of the same quality is limited. However, a significant improvement can be made by increasing data quality by reporting more complete information on the performed experiments. Furthermore, we study an in-house database with data on more than 1,000 solar cells, where the entire aging curve for each cell is available as opposed to stability metrics based on a single number. We show that the interpretation of aging experiments can strongly depend on the chosen stability metric, unnaturally favoring some cells over others. Therefore, choosing universal stability metrics is a critical question for future databases targeting this promising technology

Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset

Abstract While perovskite solar cells have reached competitive efficiency values during the last decade, stability issues remain a critical challenge to be addressed for pushing this technology towards commercialisation. In this study, we analyse a large homogeneous dataset of Maximum Power Point Tracking (MPPT) operational ageing data that we collected with a custom-built High-throughput Ageing System in the past 3 years. In total, 2,245 MPPT ageing curves are analysed which were obtained under controlled conditions (continuous illumination, controlled temperature and atmosphere) from devices comprising various lead-halide perovskite absorbers, charge selective layers, contact layers, and architectures. In a high-level statistical analysis, we find a correlation between the maximum reached power conversion efficiency (PCE) and the relative PCE loss observed after 150-hours of ageing, with more efficient cells statistically also showing higher stability. Additionally, using the unsupervised machine learning method self-organising map, we cluster this dataset based on the degradation curve shapes. We find a correlation between the frequency of particular shapes of degradation curves and the maximum reached PCE

Diltiazem vs. nicardipine on ambulatory and exercise blood pressure and on peripheral hemodynamics.

The present study was aimed at evaluating the antihypertensive efficacy of sustained-release diltiazem 180 mg vs. sustained-release nicardipine 40 mg both given twice daily. To this end 20 patients with mild to moderate hypertension were studied. After a two-week placebo period diltiazem and nicardipine were administered for 4 weeks according to a crossover design. To assess the antihypertensive efficacy of the two drugs all patients underwent Twenty-four-hour non-invasive blood pressure (BP) monitoring and a submaximal bicycle ergometric test. Ambulatory BP monitoring showed a tendency for systolic BP to be lower with nicardipine than with diltiazem during waking hours, while diastolic BP was lowered to the same extent by the two drugs. During sleep a slightly greater BP fall was observed with diltiazem. 24-hour spontaneous BP variability was slightly reduced with diltiazem and unchanged with nicardipine. Mean 24-hour heart rate was also unchanged with nicardipine and slightly reduced with diltiazem. Peripheral resistance measured by plethysmography significantly decreased with the former but not with the latter. BP and heart rate response to exercise was left unchanged by nicardipine and was slightly decreased by diltiazem. This study demonstrates that both sustained-release diltiazem and nicardipine are effective in controlling BP throughout the 24 hours without increasing BP variability. While the antihypertensive action of nicardipine was associated with a decrease of peripheral resistance, this was not the case with diltiazem

The T.O.S.CA. Project: research, education and care.

Despite recent and exponential improvements in diagnostic-therapeutic pathways, an existing "GAP" has been revealed between the "real world care" and the "optimal care" of patients with chronic heart failure (CHF). We present the T.O.S.CA. Project (Trattamento Ormonale dello Scompenso CArdiaco), an Italian multicenter initiative involving different health care professionals and services aiming to explore the CHF "metabolic pathophysiological model" and to improve the quality of care of HF patients through research and continuing medical education