Grinding circuits typically contribute disproportionately to the overall cost of ore beneficiation and their optimal operation is therefore of critical importance in the cost-effective operation of mineral processing plants. This can be challenging, as these circuits can also exhibit complex, nonlinear behavior that can be difficult to model. In this paper, it is shown that key time series variables of grinding circuits can be recast into sets of descriptor variables that can be used in advanced modelling and control of the mill. Two real-world case studies are considered. In the first, it is shown that the controller states of an autogenous mill can be identified from the load measurements of the mill by using a support vector machine and the abovementioned descriptor variables as predictors. In the second case study, it is shown that power and temperature measurements in a horizontally stirred mill can be used for online estimation of the particle size of the mill product.

Equipment throughput is one of the most critical parameters for production planning and scheduling, which is often derived by optimization techniques to achieve business goals. However, in semiconductor manufacturing, up-to-date and reliable equipment throughput is not easy to estimate and maintain because of the high complexity and extreme amount of data in the production systems. This article concerns the development and implementation of a throughput management system tailored for a semiconductor wafer fabrication plant (Fab). A brief overview of the semiconductor manufacturing and an introduction of the case Fab are presented first. Then, we focus on the system architecture and some concepts of crucial modules. This study also describes the project timescales and difficulties and discusses both tangible and intangible benefits from this project.

A process improvement in a tablet manufacturing process within a pharmaceutical industry was carried out based on an eco-efficiency approach. As it is one of the most energy consuming processes in the production line, the tablet manufacturing process was considered. It has the highest production volume with a complicated and long manufacturing product life cycle. Data were collected on energy inputs and emissions data for the stages of powder direct mixing, particle size reduction, and tableting. A straightforward approach was then used to analyze environmental impacts in terms of GHG emissions. Non- added value steps were removed from the product life cycle process, which has led to significant time and cost savings, as well as to a reduction in the emission. Annual economic savings have been achieved, a time reduction of approximately 71% was attained, and the reduction in GHG emissions and energy cost were 73.2%. The g CO₂eq per tablet reduction has been calculated within the proces... [more]

Emulsion polymerization process provides a unique polymerization locus that has a confined tiny space with a higher polymer concentration, compared with the corresponding bulk polymerization, especially for the ab initio emulsion polymerization. Assuming the ideal polymerization kinetics and a constant polymer/monomer ratio, the effect of such a unique reaction environment is explored for both conventional and living free-radical polymerization (FRP), which involves chain transfer to the polymer, forming polymers with long-chain branches. Monte Carlo simulation is applied to investigate detailed branched polymer architecture, including the mean-square radius of gyration of each polymer molecule, ₀. The conventional FRP shows a very broad molecular weight distribution (MWD), with the high molecular weight region conforming to the power law distribution. The MWD is much broader than the random branched polymers, having the same primary chain length distribution. The expected ₀ for a give... [more]

The separation of small molecule gases by membrane technologies can help performance enhancement and process intensification for emerging advanced fossil energy systems with CO₂ capture capacity. This paper reports the demonstration of controlled modification of zeolitic channel size for the MFI-type zeolite membranes to enhance the separation of small molecule gases such as O₂ and N₂. Pure-silica MFI-type zeolite membranes were synthesized on porous α-alumina disc substrates with and without an aluminum-containing thin skin on the outer surface of zeolite membrane. The membranes were subsequently modified by on-stream catalytic cracking deposition (CCD) of molecular silica to reduce the effective openings of the zeolitic channels. Such a pore modification caused the transition of gas permeation from the N₂-selective gaseous diffusion mechanism in the pristine membrane to the O₂-selective activated diffusion mechanism in the modified membrane. The experimental results indicated that th... [more]

The application of a multivariable predictive controller to the mixing process for the production of a non-Newtonian fluid is discussed in this work. A data-driven model has been developed to describe the dynamic behaviour of the rheological properties of the fluid as a function of the operating conditions using experimental data collected in a pilot plant. The developed model provides a realistic process representation and it is used to test and verify the multivariable controller, which has been designed to maintain viscosity curves of the non-Newtonian fluid within a given region of the viscosity-vs-shear rate plane in presence of process disturbances occurring in the mixing process.

Urban lakes mitigate the negative impacts on the hydrological cycle and improve the quality of life in cities. Worldwide, the concern increases for the protection and management of urban water bodies. Since the physical-chemical and biological conditions of a small aquatic ecosystem can vary rapidly over time, traditional low frequency measurement approaches (weekly or monthly sampling) limits the knowledge and the transfer of research outcomes to management decision-making. In this context, this paper presents an automatic monitoring system including a full-scale experimental site and a data transfer platform for high-frequency observations (every 5 min) in a small and shallow urban lake (Lake Champs-sur-Marne, Paris, France, 10.3 ha). Lake stratification and mixing periods can be clearly observed, these periods are compared with the dynamic patterns of chlorophyll-a, phycocyanin, dissolved oxygen and pH. The results indicate that the phytoplankton growth corresponds with dissolved ox... [more]

Density Functional Theory (DFT) studies were conducted to evaluate the sensing mechanism between benzene and a polymeric sensing material, referred to as SXFA, which contains trifluoro-groups and OH-groups. These studies were undertaken to improve the understanding of how benzene and SXFA mechanistically interact based on their chemistry, information which can be used to more efficiently design polymeric sensing materials. We find that benzene adsorbed onto the OH-groups in SXFA rather than the trifluoro-groups as previously proposed. Specifically, we find that sorption results from electrostatic attraction between the negative benzene ring and the positive hydrogens of the OH-groups of SXFA.

Studies performed at single-cell resolution have demonstrated the physiological significance of cell-to-cell variability. Various types of mathematical models and systems analyses of biological networks have further been used to gain a better understanding of the sources and regulatory mechanisms of such variability. In this work, we present a novel sensitivity analysis method, called molecular density function perturbation (MDFP), for the dynamical analysis of cellular heterogeneity. The proposed analysis is based on introducing perturbations to the density or distribution function of the cellular state variables at specific time points, and quantifying how such perturbations affect the state distribution at later time points. We applied the MDFP analysis to a model of a signal transduction pathway involving TRAIL (tumor necrosis factor-related apoptosis-inducing ligand)-induced apoptosis in HeLa cells. The MDFP analysis shows that caspase-8 activation regulates the timing of the swit... [more]

The error-in-variables-model (EVM) is the most statistically correct non-linear parameter estimation technique for reactivity ratio estimation. However, many polymer researchers are unaware of the advantages of EVM and therefore still choose to use rather erroneous or approximate methods. The procedure is straightforward but it is often avoided because it is seen as mathematically and computationally intensive. Therefore, the goal of this work is to make EVM more accessible to all researchers through a series of focused case studies. All analyses employ a MATLAB-based computational package for copolymerization reactivity ratio estimation. The basis of the package is previous work in our group over many years. This version is an improvement, as it ensures wider compatibility and enhanced flexibility with respect to copolymerization parameter estimation scenarios that can be considered.

Crystallization behavior of hexadecane (C16H34), octadecane (C18H38), eicosane (C20H42), and docosane (C22H46) dispersions of similar mean droplet diameter (x50.2 ≈ 15 µm) was investigated in quiescent systems and compared to crystallization under mechanical stress. In quiescent systems, the required supercooling decreased with increasing chain length of the alkanes to initiate crystallization. Crystallization of alkane dispersions under mechanical stress resulted in similar onset crystallization supercooling, as during quiescent crystallization. Increase of mechanical stress did not affect the onset crystallization supercooling within alkane dispersions.

Lignocellulose is an abundant and renewable resource that holds great promise for sustainable bioprocessing. However, unpretreated lignocellulose is recalcitrant to direct utilization by most microbes. Current methods to overcome this barrier include expensive pretreatment steps to liberate cellulose and hemicellulose from lignin. Anaerobic gut fungi possess complex cellulolytic machinery specifically evolved to decompose crude lignocellulose, but they are not yet genetically tractable and have not been employed in industrial bioprocesses. Here, we aim to exploit the biomass-degrading abilities of anaerobic fungi by pairing them with another organism that can convert the fermentable sugars generated from hydrolysis into bioproducts. By combining experiments measuring the amount of excess fermentable sugars released by the fungal enzymes acting on crude lignocellulose, and a novel dynamic flux balance analysis algorithm, we screened potential consortia partners by qualitative suitabilit... [more]

Dinoflagellates are the large group of marine phytoplankton with primary studies interest regarding their symbiosis with coral reef and the abilities to form harmful algae blooms (HABs). Toxin produced by dinoflagellates during events of HABs cause severe negative impact both in the economy and health sector. However, attempts to understand the dinoflagellates genomic features are hindered by their complex genome organization. Transcriptomics have been employed to understand dinoflagellates genome structure, profile genes and gene expression. RNA-seq is one of the latest methods for transcriptomics study. This method is capable of profiling the dinoflagellates transcriptomes and has several advantages, including highly sensitive, cost effective and deeper sequence coverage. Thus, in this review paper, the current workflow of dinoflagellates RNA-seq starts with the extraction of high quality RNA and is followed by cDNA sequencing using the next-generation sequencing platform, dinoflagel... [more]

This experimental and theoretical study deals with the thermal spontaneous polymerization of n-butyl acrylate (n-BA). The polymerization was carried out in solution (n-heptane as the solvent) at 200 and 220 °C without adding any conventional initiators. It was studied with the five different n-BA/n-heptane volume ratios: 50/50, 70/30, 80/20, 90/10, and 100/0. Extensive experimental data presented here show significant monomer conversion at all temperatures and concentrations confirming the occurrence of the thermal self-initiation of the monomer. The order, frequency factor, and activation energy of the thermal self-initiation reaction of n-BA were estimated from n-BA conversion, using a macroscopic mechanistic model. The estimated reaction order agrees well with the order obtained via our quantum chemical calculations. Furthermore, the frequency factor and activation energy estimates agree well with the corresponding values that we already reported for bulk polymerization of n-BA.

Using experimental data obtained from in vitro bioaugmentation studies of a sand filter community of 13 bacterial species, we develop an individual-based model representing the in silico counterpart of this synthetic microbial community. We assess the inter-species interactions, first by identifying strain identity effects in the data then by synthesizing these effects into a competition structure for our model. Pairwise competition outcomes are determined based on interaction effects in terms of functionality. We also consider non-deterministic competition, where winning probabilities are assigned based on the relative intrinsic competitiveness of each strain. Our model is able to reproduce the key qualitative dynamics observed in in vitro experiments with similar synthetic sand filter communities. Simulation outcomes can be explained based on the underlying competition structures and the resulting spatial dynamics. Our results highlight the importance of community diversity and in pa... [more]

The problem of scheduling multiproduct and multipurpose batch processes has been studied for more than 30 years using math programming and heuristics. In most formulations, the manufacturing recipes are represented by simplified models using state task network (STN) or resource task network (RTN), transfers of materials are assumed to be instantaneous, constraints due to shared utilities are often ignored, and scheduling horizons are kept small due to the limits on the problem size that can be handled by the solvers. These limitations often result in schedules that are not actionable. A simulation model, on the other hand, can represent a manufacturing recipe to the smallest level of detail. In addition, a simulator can provide a variety of built-in capabilities that model the assignment decisions, coordination logic and plant operation rules. The simulation based schedules are more realistic, verifiable, easy to adapt for changing plant conditions and can be generated in a short perio... [more]

In this study, the solvent based extraction of artemisinin from Artemisia annua L. using acetone in percolation mode is compared to the method of pressurized hot water extraction. Both techniques are simulated by a physico-chemical process model. The model as well as the model parameter determination, including the thermal degradation of artemisinin are shown and discussed. For the conventional extraction, a solvent screening is performed considering various organic solvents. A temperature screening is presented for the systematic design of the pressurized hot water extraction. The best temperature with regards to thermal decomposition and high productivity was found to be 80 °C. Both, conventional percolation and Pressurized Hot Water Extraction (PHWE) are suitable for the extraction of artemisinin. The extraction curves show a high conformity with the simulation results.

Dynamic optimization offers a great potential for maximizing performance of continuous processes from startup to shutdown by obtaining optimal trajectories for the control variables. However, numerical procedures for dynamic optimization can become prohibitively costly upon a sufficiently fine discretization of control trajectories, especially for large-scale dynamic process models. On the other hand, a coarse discretization of control trajectories is often incapable of representing the optimal solution, thereby leading to reduced performance. In this paper, a new control discretization approach for dynamic optimization of continuous processes is proposed. It builds upon turnpike theory in optimal control and exploits the solution structure for constructing the optimal trajectories and adaptively deciding the locations of the control discretization points. As a result, the proposed approach can potentially yield the same, or even improved, optimal solution with a coarser discretization... [more]

A novel formulation for combined scheduling and control of multi-product, continuous chemical processes is introduced in which nonlinear model predictive control (NMPC) and noncyclic continuous-time scheduling are efficiently combined. A decomposition into nonlinear programming (NLP) dynamic optimization problems and mixed-integer linear programming (MILP) problems, without iterative alternation, allows for computationally light solution. An iterative method is introduced to determine the number of production slots for a noncyclic schedule during a prediction horizon. A filter method is introduced to reduce the number of MILP problems required. The formulation’s closed-loop performance with both process disturbances and updated market conditions is demonstrated through multiple scenarios on a benchmark continuously stirred tank reactor (CSTR) application with fluctuations in market demand and price for multiple products. Economic performance surpasses cyclic scheduling in all scenarios... [more]

Performance of integrated production scheduling and advanced process control with disturbances is summarized and reviewed with four progressive stages of scheduling and control integration and responsiveness to disturbances: open-loop segregated scheduling and control, closed-loop segregated scheduling and control, open-loop scheduling with consideration of process dynamics, and closed-loop integrated scheduling and control responsive to process disturbances and market fluctuations. Progressive economic benefit from dynamic rescheduling and integrating scheduling and control is shown on a continuously stirred tank reactor (CSTR) benchmark application in closed-loop simulations over 24 h. A fixed horizon integrated scheduling and control formulation for multi-product, continuous chemical processes is utilized, in which nonlinear model predictive control (NMPC) and continuous-time scheduling are combined.

Dry reforming is a very interesting process for synthesis gas generation from CH 4 and CO 2 but suffers from low hydrogen yields due to the reverse water⁻gas shift reaction (WGS). For this reason, membranes are often used for hydrogen separation, which in turn leads to coke formation at the process temperatures suitable for the membranes. To avoid these problems, this work shows the possibility of using nickel self-supported membranes for hydrogen separation at a temperature of 800 ∘ C. The higher temperature effectively suppresses coke formation. The paper features the analysis of the dry reforming reaction in a nickel membrane reactor without additional catalyst. The measurement campaign targeted coke formation and conversion of the methane feedstock. The nickel approximately 50% without hydrogen separation. The hydrogen removal led to an increase in methane conversion to 60⁻90%.

Differential activation of neuronal populations can improve the efficacy of clinical devices such as sensory or cortical prostheses. Improving stimulus specificity will facilitate targeted neuronal activation to convey biologically realistic percepts. In order to deliver more complex stimuli to a neuronal population, stimulus optimization techniques must be developed that will enable a single electrode to activate subpopulations of neurons. However, determining the stimulus needed to evoke targeted neuronal activity is challenging. To find the most selective waveform for a particular population, we apply an optimization-based search routine, Powell’s conjugate direction method, to systematically search the stimulus waveform space. This routine utilizes a 1-D sigmoid activation model and a 2-D strength⁻duration curve to measure neuronal activation throughout the stimulus waveform space. We implement our search routine in both an experimental study and a simulation study to characterize... [more]

Tuberculosis (TB) is one of the most common infectious diseases worldwide. It is estimated that one-third of the world’s population is infected with TB. Most have the latent stage of the disease that can later transition to active TB disease. TB is spread by aerosol droplets containing Mycobacterium tuberculosis (Mtb). Mtb bacteria enter through the respiratory system and are attacked by the immune system in the lungs. The bacteria are clustered and contained by macrophages into cellular aggregates called granulomas. These granulomas can hold the bacteria dormant for long periods of time in latent TB. The bacteria can be perturbed from latency to active TB disease in a process called granuloma activation when the granulomas are compromised by other immune response events in a host, such as HIV, cancer, or aging. Dysregulation of matrix metalloproteinase 1 (MMP-1) has been recently implicated in granuloma activation through experimental studies, but the mechanism is not well understood.... [more]

Calculation of evaporation requires accurate thermophysical properties of the liquid. Such data are well-known for conventional fossil fuels. In contrast, e.g., thermal conductivity or dynamic viscosity of the fuel vapor are rarely available for modern liquid fuels. To overcome this problem, molecular models can be used. Currently, the measurement-based properties of n-heptane and diesel oil are compared with estimated values, using the state-of-the-art molecular models to derive the temperature-dependent material properties. Then their effect on droplet evaporation was evaluated. The critical parameters were liquid density, latent heat of vaporization, boiling temperature, and vapor thermal conductivity where the estimation affected the evaporation time notably. Besides a general sensitivity analysis, evaporation modeling in a practical burner ended up with similar results. By calculating droplet motion, the evaporation number, the evaporation-to-residence time ratio can be derived. A... [more]

The pennate diatom Phaeodactylum tricornutum is a model organism able to synthesize industrially-relevant molecules. Commercial-scale cultivation currently requires large monocultures, prone to bio-contamination. However, little is known about the identity of the invading organisms. To reduce the complexity of natural systems, we systematically investigated the microbiome of non-axenic P. tricornutum cultures from a culture collection in reproducible experiments. The results revealed a dynamic bacterial community that developed differently in “complete” and “minimal” media conditions. In complete media, we observed an accelerated “culture crash”, indicating a more stable culture in minimal media. The identification of only four bacterial families as major players within the microbiome suggests specific roles depending on environmental conditions. From our results we propose a network of putative interactions between P. tricornutum and these main bacterial factions. We demonstrate that,... [more]