Multivariate Synergies in Pharmaceutical Roll Compaction : The quality influence of raw materials and process parameters by design of experiments

Roll compaction is a continuous process commonly used in the pharmaceutical industry for dry granulation of moisture and heat sensitive powder blends. It is intended to increase bulk density and improve flowability. Roll compaction is a complex process that depends on many factors, such as feed powder properties, processing conditions and system layout. Some of the variability in the process remains unexplained. Accordingly, modeling tools are needed to understand the properties and the interrelations between raw materials, process parameters and the quality of the product. It is important to look at the whole manufacturing chain from raw materials to tablet properties. The main objective of this thesis was to investigate the impact of raw materials, process parameters and system design variations on the quality of intermediate and final roll compaction products, as well as their interrelations. In order to do so, we have conducted a series of systematic experimental studies and utilized chemometric tools, such as design of experiments, latent variable models (i.e. PCA, OPLS and O2PLS) as well as mechanistic models based on the rolling theory of granular solids developed by Johanson (1965). More specifically, we have developed a modeling approach to elucidate the influence of different brittle filler qualities of mannitol and dicalcium phosphate and their physical properties (i.e. flowability, particle size and compactability) on intermediate and final product quality. This approach allows the possibility of introducing new fillers without additional experiments, provided that they are within the previously mapped design space. Additionally, this approach is generic and could be extended beyond fillers. Furthermore, in contrast to many other materials, the results revealed that some qualities of the investigated fillers demonstrated improved compactability following roll compaction. In one study, we identified the design space for a roll compaction process using a risk-based approach. The influence of process parameters (i.e. roll force, roll speed, roll gap and milling screen size) on different ribbon, granule and tablet properties was evaluated. In another study, we demonstrated the significant added value of the combination of near-infrared chemical imaging, texture analysis and multivariate methods in the quality assessment of the intermediate and final roll compaction products. Finally, we have also studied the roll compaction of an intermediate drug load formulation at different scales and using roll compactors with different feed screw mechanisms (i.e. horizontal and vertical). The horizontal feed screw roll compactor was also equipped with an instrumented roll technology allowing the measurement of normal stress on ribbon. Ribbon porosity was primarily found to be a function of normal stress, exhibiting a quadratic relationship. A similar quadratic relationship was also observed between roll force and ribbon porosity of the vertically fed roll compactor. A combination of design of experiments, latent variable and mechanistic models led to a better understanding of the critical process parameters and showed that scale up/transfer between equipment is feasible

Categorization of tars from fast pyrolysis of pure lignocellulosic compounds at high temperature

This study presents how the yields of different tar compounds from pure lignocellulosic compounds respond to the change in temperature and residence time. Experiments were carried out with a drop tube furnace in the temperature range from 800 to 1250 °C. The tar composition was characterized by gas chromatography with a flame ionization detector and mass spectrometry using a dual detector system. Longer residence time and higher heat treatment temperatures increased the soot formation and decreased the tar yields. Soot yields from lignin samples were greater than soot yields from holocellulose pyrolysis. The dominating products in tars from pyrolysis of all lignocellulosic compounds were benzene and toluene. Cellulose and hemicellulose pyrolysis produced greater amount of oxygenates in tars, whereas lignin tar was rich in phenols, polycyclic hydrocarbons and naphthalenes. Simultaneous reduction of tar and soot was achieved by impregnation of lignin from wheat straw with alkali metals. The OPLS-DA model can accurately explain the differences in tar composition based on the experimental mass spectrometry data.The authors gratefully acknowledge financial support from the Kempe Foundations and the Swedish strategic research program Bio4Energy. We are grateful to the plant cell wall and carbohydrate analytical facility at UPSC/SLU, supported by Bio4Energy and TC4F for the GC-MS analysis. The authors acknowledge the facilities and technical support of Dr. Junko Takahashi-Schmidt at Umeå Plant Science Centre. Dr. Flemming Hofmann Larsen and Dr. Soren Talbro Barsberg at University of Copenhagen are acknowledged for the technical support with NMR and FTIR analysis. We acknowledge Raymond Mclnerney from University of Limerick for the article proof-reading.2021-04-1

Categorization of tars from fast pyrolysis of pure lignocellulosic compounds at high temperature

This study presents how the yields of different tar compounds from pure lignocellulosic compounds respond to the change in temperature and residence time. Experiments were carried out with a drop tube furnace in the temperature range from 800 to 1250 °C. The tar composition was characterized by gas chromatography with a flame ionization detector and mass spectrometry using a dual detector system. Longer residence time and higher heat treatment temperatures increased the soot formation and decreased the tar yields. Soot yields from lignin samples were greater than soot yields from holocellulose pyrolysis. The dominating products in tars from pyrolysis of all lignocellulosic compounds were benzene and toluene. Cellulose and hemicellulose pyrolysis produced greater amount of oxygenates in tars, whereas lignin tar was rich in phenols, polycyclic hydrocarbons and naphthalenes. Simultaneous reduction of tar and soot was achieved by impregnation of lignin from wheat straw with alkali metals. The OPLS-DA model can accurately explain the differences in tar composition based on the experimental mass spectrometry data.The authors gratefully acknowledge financial support from the Kempe Foundations and the Swedish strategic research program Bio4Energy. We are grateful to the plant cell wall and carbohydrate analytical facility at UPSC/SLU, supported by Bio4Energy and TC4F for the GC-MS analysis. The authors acknowledge the facilities and technical support of Dr. Junko Takahashi-Schmidt at Umeå Plant Science Centre. Dr. Flemming Hofmann Larsen and Dr. Soren Talbro Barsberg at University of Copenhagen are acknowledged for the technical support with NMR and FTIR analysis. We acknowledge Raymond Mclnerney from University of Limerick for the article proof-reading.peer-reviewed2021-04-1

A machine learning framework to improve effluent quality control in wastewater treatment plants

Due to the intrinsic complexity of wastewater treatment plant (WWTP) processes, it is always challenging to respond promptly and appropriately to the dynamic process conditions in order to ensure the quality of the effluent, especially when operational cost is a major concern. Machine Learning (ML) methods have therefore been used to model WWTP processes in order to avoid various shortcomings of conventional mechanistic models. However, to the best of the authors' knowledge, no ML applications have focused on investigating how operational factors can affect effluent quality. Additionally, the time lags between process steps have always been neglected, making it difficult to explain the relationships between operational factors and effluent quality. Therefore, this paper presents a novel ML-based framework designed to improve effluent quality control in WWTPs by clarifying the relationships between operational variables and effluent parameters. The framework consists of Random Forest (RF) models, Deep Neural Network (DNN) models, Variable Importance Measure (VIM) analyses, and Partial Dependence Plot (PDP) analyses, and uses a novel approach to account for the impact of time lags between processes. Details of the framework are provided along with a demonstration of its practical applicability based on a case study of the Umeå WWTP in Sweden involving a large number of samples (105763) representing the full scale of the plant's operations. Two effluent parameters, Total Suspended Solids in effluent (TSSe) and Phosphate in effluent (PO4e), and thirty-two operational variables are studied. RF models are developed, validated using DNN models as references, and shown to be suitable for VIM and PDP analyses. VIM identifies the variables that most strongly influence TSSe and PO4e, while PDP elucidates their specific effects on TSSe and PO4e. The major findings are: (1) Influent temperature is the most influential variable for both TSSe and PO4e, but it affects them in different ways; (2) PO4e depends strongly on the TSS in aeration basins – higher TSS concentrations in aeration basins generally promote PO4 removal, but excess TSS can have negative effects; (3) In general, the impact of TSS in aeration basins on TSSe and PO4e increases with the distances of the basin from the merging outlet, so more attention should be paid to the TSS concentration in the third or fourth aeration basins than the first and second ones; (4) Returning excessive amounts of sludge through the second return sludge pipe should be avoided because of its adverse impact on TSSe removal. These results could support the development of more advanced control strategies to increase control precision and reduce running costs in the Umeå WWTP and other similarly configured WWTPs. The framework could also be applied to other parameters in WWTPs and industrial processes in general if sufficient high-resolution data are available

Design Space Estimation of the Roller Compaction Process

Roller compaction (RC) is a continuous process for solid dosage form manufacturing within the pharmaceutical industry achieving similar goals as wet granulation while avoiding liquid exposure. From a quality by design perspective, the aim of the present study was to demonstrate the applicability of statistical design of experiments (DoE) and multivariate modeling principles to identify the Design Space of a roller compaction process using a predictive risk-based approach. For this purpose, a reduced central composite face-centered (CCF) design was used to evaluate the influence of roll compaction process variables (roll force, roll speed, gap width, and screen size) on the different intermediate and final products (ribbons, granules, and tablets) obtained after roll compaction, milling, and tableting. After developing a regression model for each response, optimal settings were found which comply with the response criteria. Finally, a predictive risk based approach using Monte Carlo simulation of the factor variability and its influence on the responses was applied which fulfill the criteria for the responses in a space where there is a low risk for failure. Responses were as follows: granule throughput, ribbon porosity, granules particle size, and tablets tensile strength. The multivariate method orthogonal partial least-squares (OPLS) was used to model product dependencies between process steps e.g. granule properties with tablet properties. Those results confirmed that the tensile strength reduction, known to affect plastic materials when roll compacted, was not prominent when using brittle materials. While direct compression qualities are frequently used for roll compacted drug products because of their excellent flowability and good compaction properties, this study confirmed earlier findings that granules from these qualities were more poor flowing than the corresponding powder blend