Operational Process Variables Research Articles

Evaluation of Selected Operating Process Variables for a Bioflocculant Supported Column Flotation System

The poultry industry generates significant volumes of slaughterhouse wastewater, laden with numerous pollutants, thus requiring pretreatment prior to discharge. However, new technologies must be used to re-engineer the existing wastewater treatment equipment and incorporate new designs to improve the treatment processes or system performance. In this study, three variables, i.e., diffuser design, bioflocculant form, and flow rate, were evaluated to determine their effect on the performance of a bioflocculant-supported column flotation (BioCF) system. It was found that bioflocculants influenced diffuser performance with limited impact when the feed flow rate was varied, i.e., 3D-printed air diffusers and cell-free flocculants imparted high BioCF performance when compared to moulded diffusers and cell-bound flocculants. Notably, the combination of 3D-printed air diffusers and cell-free flocculants resulted in relatively high pollutant removal (81.23% COD, 94.44% TSS, 97.77% protein, and 90.38% turbidity reduction). The study lays a foundation for exploring 3D-printed air diffusers, a relatively new technology in conjunction with microbial flocculants usage that are regarded as eco-friendly for application in industry to enhance the performance of column flotation systems.

Removal of fluoride from ground water by electrocoagulation method: investigation of process parameters, kinetic analysis, and operating cost

In the current study, aluminum electrodes have been employed in electrocoagulation (EC) to remove fluoride ions from groundwater. The effects of various operating process variables of EC, including pH, salt dosage, stirring speed, inter-electrode distance, and current density have been studied, as well as the operating cost and kinetics of the process. The most favorable values of the EC process parameters have been observed, which are effective for the removal of 97.6% fluoride, such as an initial value of pH 7.0, a current density value of 105.86 A/m2, an IED of 1 cm, a dose of salt of 0.33 g/L, a rotating speed of 700 RPM, and a time 40 min. The operating cost for the removal of fluoride (97.6% efficiency) from groundwater by EC was found to be 0.27 US$/m3. The effectiveness of the EC process in the removal of turbidity and TDS from different operating circumstances have also been investigated. First-order kinetics was observed in the kinetic study. The reaction rate constant k increases with CD increase, k decreases with IED increase, and k decreases with initial concentration increase. EDX analysis and FTIR spectra of the sludge confirm the availability of trace amounts of fluoride in the sludge produced. The EC process can effectively reduce the fluoride concentration from synthetic water as well as real groundwater up to below 1.5 mg/L as per WHO recommendations for drinking water.

Process analytical technologies for precise, timely and representative elemental and moisture measurement for conveyed flows

Industrial and technological processes are very difficult to manage when the quality of feed and product or discard are not measured with confidence. Effective control can occur when process analytical technologies are chosen that provide representative, precise, and timely measurements. For the measurement technique to be representative it must comply with the Theory of Sampling (TOS) and provide an equal chance of any component in the streaming material to be included in the support for the measurement. This generally precludes technologies that measure only the surface of materials, or biased measurements stemming from a limited portion of the material only, particularly in the minerals processing and recycling sectors, which usually display high compositional variability. The location of the analytical technology should relate to the benefit being targeted and allow for enough reaction time to respond to the quality in some way; diverting short increments based on composition and decision parameters based on process impact, blending with other quality materials, or feeding information backwards or forwards. Feed forward options can include flow rate control, reagent control, operational process variables that impact recoveries, etc. Major benefits have been achieved in measuring coarse conveyed flows with high specification Prompt Gamma Neutron Activation Analysis (PGNAA) over short increments (thirty seconds to two minutes) for most elements, or over five to ten minute increments for trace elements, such as gold. PGNAA applied to conveyed flows allow the full flow to be measured continuously and composition averaged for each increment in real time. The use of penetrative and continuous moisture measurement using transmission microwaves has also proved effective for moisture monitoring and management. Precisions between laboratory samples of the flow and analysis data from analysers can be sufficient to have high confidence in resulting process control decisions. This paper explains the benefits in more detail and includes case studies to highlight actual benefits derived from the application of such systems. It should be noted that sampling of the materials is still required for calibration and adjustments for the process analytical tools.

Identifying associations between health services operational factors and health experience for patients with type 2 diabetes in Iran

BackgroundFacing limited health resources, healthcare providers need to rely on health service delivery models that produce the best clinical outcomes and patient experience. We aimed to contribute to developing a patient experience-based type 2 diabetes service delivery model by identifying operational structures and processes of care that were associated with clinical outcome, health experience, and service experience.MethodsWe conducted a cross-sectional survey of type 2 diabetes patients between January 2019 to February 2020. Having adjusted for demand variables, we examined relationships between independent variables (behaviours, services/processes, and structures) and three categories of dependent variables; clinical outcomes (HbA1c and fasting blood glucose), health experience (EuroQol quality of life (EQ-5D), evaluation of quality of life (visual analgene scale of EQ-5D), and satisfaction with overall health status), and service experience (evaluation of diabetes services in comparison with worst and best imaginable diabetes services and satisfaction with diabetes services). We analysed data using multivariate linear regression models using Stata software.ResultsAfter adjusting for demand variables; structures, diabetes-specific health behaviours, and processes explained up to 22, 12, and 9% of the variance in the outcomes, respectively. Based on significant associations between the diabetes service operations and outcomes, the components of an experience-based service delivery model included the structural elements (continuity of care, redistribution of task to low-cost resources, and improved access to provider), behaviours (improved patient awareness and adherence), and process elements (reduced variation in service utilization, increased responsiveness, caring, comprehensiveness of care, and shared decision-making).ConclusionsBased on the extent of explained variance and identified significant variables, health services operational factors that determine patient-reported outcomes for patients with type 2 diabetes in Iran were identified, which focus on improving continuity of care and access to providers at the first place, improving adherence to care at the second, and various operational process variables at the third place.

Analysis of Wall Thickness Eccentricity in the Rotary Tube Piercing Process Using a Strain Correlated FE Model

The wall thickness eccentricity is one of the most important weaknesses that appears in seamless tubes production, since this imperfection is subsequently transferred downstream through the manufacturing stages until the final product. For this reason, in this article a finite element model of the rotary tube piercing (RTP) process is developed aimed at analysing the wall thickness eccentricity imperfection. Experimental data extracted from the industrial process is used for the validation of the model, including operational process variables like power consumption and process velocity, and deformation variables as elongation and longitudinal torsion, originated by axial and shear strain respectively. The cause of longitudinal torsion is also analysed. The two most important conclusions derived from this study are: (I) the longitudinal torsion of the tube is a crucial parameter for the correct model validation, and (II) the combined effect between the uneven temperature distribution of the billet and the plug bending deformation is identified as the major cause of the wall thickness eccentricity flaw.

Artificial Intelligence Assisted Dynamic Control of Environmental Emissions From Hybrid Energy Process Plants (HEPP)

A model digital data processing platform is proposed based on “deep-learning” methodology that can be used to identify the emissions patterns from process plants with hybrid energy recovery and energy generation facilities. The non-invasive dynamic monitoring and correlation of particulate, VOCs and other greenhouse gas emissions from semi-batch and continuous process plants is demonstrated with use of neural encoding and pattern recognition using a multi-layer perceptron and multi-stack encoder configuration. A multi-layer environmental perceptron (MLEP) is developed based on the above analyses that aims to detect patterns of emission types, rates and concentrations as a function of variation of plant operational conditions and process variables. As a further consequence, environmental impact assessment is considered within the hazards and process safety frameworks that conventionally issue sanctions and penalize non-compliance with imposition of environmental levy scales rather than offering process improvement incentives. The latter is demonstrated to be possible by facilitating dynamic corrective action and hazard prevention using MLEP platforms should emission ceilings be frequently and/or periodically exceeded in 24/7 continuous plant operations. Potential applications of the multi-layer environmental perceptron (MLEP) are illustrated in the context of dynamic emissions control and abatement in hybrid energy process plants (HEPP) and combined power plants using process-integrated CO2 capture and storage schemes.

Techno-economic analysis of caffeine and catechins production from black tea waste

This work establishes a proof of concept level economic assessment of a novel commercial production scheme for caffeine and catechins from black tea waste collected in the north-eastern region of Turkey. A base case in which dichloromethane (DCM) is used as the product extracting agent and an alternative greener case where DCM is replaced with environmentally friendlier ethyl acetate (EA) were designed and modelled on Superpro Designer platform. Profitability analyses were conducted for both cases, each treating 30,000MT/year of black tea waste. Under the base case scenario, the Net Present Value (NPV) was calculated as 16,249,000 USD whereas the alternative scenario gave an NPV of 4,379,000 USD with corresponding payback times of 5.64 and 6.79 years. A novel techno-economic evaluation method involving the metamodeling of Superpro Designer models via Kriging and subsequent multi-objective optimization is incorporated into the economic assessment and applied to the two cases in order to optimize selected dominant process variables. Optimal values for dominant operating process variables were found using 3-D graphs simulating the combined effects of these variables on NPV. Results showed that the base case has a greater potentiality to generate profit. However, the greener case could be implemented if it is supported on environmental and toxicity issues considering the potential health and pollution preventive benefits. The present work demonstrates how the integration of novel conceptual design, modelling, and optimization approaches can foster broader evaluation of bioprocesses utilizing negative-value agro-wastes for the production of value-added chemicals.

Photocatalytic performance and antibacterial activity of visible light driven silver iodide anchored on Graphitic-C3N4 binary composite

This study mainly focuses on the photocatalytic and antimicrobial properties of silver iodide supported graphitic carbon nitride (AgI@g-C3N4) composite. AgI@g-C3N4 composite was obtained through modified deposition-precipitation method and the photocatalytic performance efficiency of the synthesized AgI@g-C3N4 composite was evaluated for degradation of methyl orange (MO) dye solution under visible light irradiation. A central composite design (CCD) with response surface methodology (RSM) optimization tool was used to investigate the interactive effects of the three main independent rate determining operational process variables such as pH, AgI@g-C3N4 dosage and initial MO concentration on the colour removal efficiency. The maximum decolorization efficiency of 94.75% was obtained at pH 8.42, 2.34 g/L of AgI@g-C3N4 dosage and 17.7 mg/L of initial MO concentration. All the experimental values of decolorization efficiency were found to be in good agreement with predicted values obtained from CCD analysis (R2 = 0.9978). Furthermore, the antimicrobial activity for AgI@g-C3N4 composite in the elimination of Escherichia coli (E. coli) bacteria was studied using disc diffusion method.

Electrocoagulation process for propiconazole elimination from wastewater: experimental design for correlative modeling and optimization

Electrocoagulation process is an electrochemical method to remove variety of contaminants (i.e., pesticides) from water and wastewater effluents. The current research examines the elimination of propiconazole by electrocoagulation technique. For this aim, after the preliminary screening tests (using factorial design) for the identification of the significant and remarkable factors, response surface methodology based on central composite design is employed to develop a correlative model for the efficiency of the electrocoagulation process. Furthermore, simultaneous optimization of the operational process variables [electrolysis duration (10–60 min), propiconazole initial concentration (10–50 mg/L), current density (2.5–12.5 mA/cm2), and solution conductivity (0.5–2.5 mS/cm)], is carried out to find the most appropriate strategy. The screening of the prime considered factors indicates that pH is not a significant variable and consequently is excluded from further deliberations. A quadratic correlative model is prepared, and model-based optimization is further performed and experimentally verified. The maximum removal efficiency (79.83%) is attained regarding the following process factors: the propiconazole initial concentration of 25.93 mg/L, the time duration of 36.44 min, the current density of 10.95 mA/cm2, and the solution conductivity of 2.44 mS/cm. The results demonstrate that through electrocoagulation process with proper operational variables, the amounts of propiconazole in wastewater effluents can adequately get lowered to acceptable levels.

An integrated approach for dynamic economic risk assessment of process systems

Abstract This paper proposes a dynamic economic risk analysis methodology for process systems. The Bayesian Tree Augmented Naive Bayes (TAN) algorithm is applied to model the precise and concise probabilistic dependencies that exist among key operational process variables to detect faults and predict the time dependent probability of system deviation. The modified inverted normal loss function is used to define system economic losses as a function of process deviation. The time dependent probability of system deviation owing to an abnormal event is constantly updated based on the present state of the relevant process variables. The integration of real time probability of system deviation with potential losses provides the risk profile of the system at any instant. This risk profile can be used as the basis for operational decision making and also to activate the emergency safety system. The proposed methodology is tested and verified using the Richmond refinery accident.

Sanitary landfill leachate treatment using combined solar photo-Fenton and biological oxidation processes at pre-industrial scale

This work proposes a new strategy for the treatment of leachates from sanitary landfills after lagooning pretreatment, using a solar photo-Fenton oxidation process to eliminate the most recalcitrant organic compounds, leading to a biodegradability enhancement of the leachate and promoting its subsequent oxidation in an activated sludge biological reactor. The integrated leachate treatment process was conducted in a pre-industrial plant, incorporating a photocatalytic system with 39.52m2 of compound parabolic collectors (CPCs) and an activated sludge biological reactor, with 3.5m3 capacity, operated under aerated and anoxic conditions. An extensive physico-chemical characterization of the leachate after lagooning was performed during one year, from June 2010 to May 2011, showing its high recalcitrant character mainly associated with the presence of humic substances.The efficiency of the combined treatment was evaluated concerning the leachate characteristics’ variability after lagooning, availability of solar radiation during the year, and different operational process variables, such as the amount of hydrogen peroxide necessary to reach the required COD target value, biodegradability enhancement during the photo-oxidation process, iron reutilization in consecutive oxidation processes, removal of acidic sludge resulting from the acidification process and leachate temperature/average solar power. The elimination of the remaining organic carbon fraction and nitrogen compounds after the pre-oxidation step was also assessed in an activated sludge biological reactor, under aerobic and anoxic conditions, considering the composition variability of photo-treated leachate. Nitrification and denitrification rates were also evaluated.

Optimization of Freeze-Drying Cycles of Pharmaceutical Organic Co-Solvent-Based Formulations Using the Design Space Methodology

This paper deals with the concept of Design Space, which is an efficient tool for freeze-drying cycle optimization that gathers a combination of product formulation variables and of operating process variables that provide suitable quality attributes of the final freeze-dried product. The Design Space concept is presented as an envelope in a graph, with sublimation rates and total gas chamber pressures on the main (x,y) axis, illustrating the functional relationships between the sublimation rates, the mean product temperatures, and the two principal independent process variables, namely the shelf heat transfer fluid temperature and the total gas pressure. The limits of this design space are determined by the influence of product and process variables on quality attributes of the freeze-dried drug, more precisely by the failure of these attributes under aggressive cycle conditions. In the case of large industrial freeze-dryers, other limits are also imposed by freeze-dryer performances. Next, as an illustration of this concept, we have presented a case study showing the methodology of construction of the Design Space for an ibuprofen organic co-solvent-based formulation realized with a pilot laboratory freeze-dryer. This methodology can reduce the development and the cycle optimization costs of drugs’ freeze-drying processes, especially in the case of costly active principle ingredients that are only available in small quantities.

Steady-state operational degrees of freedom in absorption chillers and heat pumps: Methodology and case study

The main purpose of modelling and analysing absorption chillers and heat pumps in operation is to reproduce the system performance using operational data. To perform this analysis, the first two important issues are finding the number of degrees of freedom of the system model and identifying which set of operational process variables are appropriate to reduce the available degrees of freedom to zero. Applying the conventional method of finding degrees of freedom for advanced cyclic processes is a complex, confusing and time consuming process. This paper proposes a simple and straight forward systematic method for determining the degrees of freedom and a way of selecting the appropriate set of process variables to close the mathematical models developed for absorption chillers and heat pumps in operation. The method is applied to different examples of absorption cycles ranging from simple to advanced (complex) configurations. It is also applied to a small scale ammonia-water absorption chiller in a pilot plant to demonstrate the systematic selection of operational process variables for developing a well-posted model for absorption chillers and heat pumps in operation.

Interorganizational Networks: Using a Theoretical Model to Predict Effectiveness of Rural Health Care Delivery Networks

Interorganizational health care delivery networks have potential for sustaining health services delivery in rural areas faced with economic and demographic challenges. Four Nebraska rural health care delivery networks (Albion-Ord, Blue River Valley. Rural Partners. Inc., and Western Nebraska) were compared to an interorganizational model based on theories of interorganizational relations, exchange, population ecology, and synthesized collaboration. It assumes that outcomes, including effectiveness, are influenced by external and internal factors that are operationalized through external control, technology, structure, and operational process variables. Data were collected by a non-random, two-level cluster mail survey of network, members (45/59 = 76.3% response rate). All networks received technical assistance from the Nebraska Office of Rural Health. Networks have formal organization, strategic plans, and official coordinators. Hospital administrators hold most leadership positions; few doctors or citizens are involved. Correlation and multiple regression analysis show partial fit between the research model and study networks. Effectiveness, measure by the gap between best possible and actual practice, increased with network connectivity (r=.36, p<.05), group methods of administrative decisionmaking (r=.52, p<.001) and sequential pattern of service delivery (r=.39, p<.05). Greater dependence on vertical funding corresponds to greater external control (r=.43, p<.01). The prediction that, as scope narrows, task intensity (r=.56, p<.001). duration (r=.41, p<.01), and task volume (r=.50, p<01) increase is upheld. Centrality and network size decrease together (r=.43, p<01) where there is little reliance on vertical sources of funds (r=.36, p<.05). The integrated interorganizational model demonstrates some efficacy for testing potential effectiveness of networks.

A tuned approach of the predictive–adaptive GPC controller applied to a fed-batch bioreactor using complete factorial design

This work presents a novel tuned approach of the Generalized Predictive Control controller in both adaptive and nonadaptive configurations applied to a fed-batch penicillin process using the complete factorial design method. The controller stabilizes the dissolved oxygen concentration through agitation manipulation. The operating process variables were calculated by a mathematical model solved numerically. This new approach employed complete factorial design methodology to provide the optimal set of parameters, estimating the design parameters influence on the integral of the absolute error between the controlled variable and the set point (IAE). The controller parameters analyzed were prediction and control horizons, suppression factor, reference trajectory and integral factor. Moreover, the robustness performance was evaluated using the white noise presence in the measured variable, different polynomial orders of the internal model, several delay time and sampling periods of the controlled variable, and the presence of the internal polynomial. This controller performed better than PID and DMC controllers

Pareto design of conveyor-belt dryers

Most research efforts in the field of conveyor-belt dryer design focus on the evaluation of the appropriate structural and operational process variables so that total annual plant cost involved is optimized. However, the increasing need for dehydrated products of the highest quality imposes the development of new criteria that together with cost components, determine the design rules for any drying process. Quality of dehydrated products is a complex resultant of properties characterizing the final products, where the most important one is color. Color is determined as a three-parameter resultant, whose values for products, which have undergone drying should deviate from the corresponding ones of natural products, as little as possible. In this case, conveyor-belt dryer design is a complex multi-objective optimization problem, involving the color deviation and the unit cost of final product as an objective vector and as constraints the ones deriving from the process mathematical model. The mathematical model of the dryer was developed and the fundamental color deterioration laws and critical financial parameters were determined for the conveyor-belt drying process. Non-preference multi-criteria optimization methods were used and the Pareto optimal set of efficient solutions was evaluated. An example covering the drying of sliced potato was included to demonstrate the performance of the design procedure, as well as the effectiveness of the proposed approach.

Pareto design of fluidized bed dryers

Most of the research efforts in the field of fluidized dryer design, focus on the evaluation of the appropriate structural and operational process variables so that total annual plant cost involved is optimized. However, the increasing need for dehydrated products of the highest quality, imposes the development of new criteria that, together with cost components, determine the design rules for any drying process. Quality of dehydrated products is a complex resultant of properties characterizing the final products, where the most important one is color. Color is represented by three parameters: redness, yellowness and lightness. These three parameters of a dried product should deviate from that of its original material as little as possible. In this case, fluidized bed dryer design is a complex multi-objective optimization problem, involving the color deviation and the unit cost of final product as an objective vector and as constraints described in process models. The mathematical model of the dryer was developed and the fundamental color deterioration laws and critical financial parameters were determined for the fluidized bed drying process. In this paper, non-preference multi-criteria optimization methods were used and the Pareto-optimal set of efficient solutions was evaluated. An example for drying of sliced potato was studied in detail to demonstrate the design procedure, the process performance as well as the effectiveness of the proposed approach.

PRODUCT QUALITY MULTI-OBJECTIVE OPTIMIZATION OF FLUIDIZED BED DRYERS

ABSTRACT Design of fluidized bed dryers constitutes a mathematical programming problem involving the evaluation of appropriate structural and operational process variables so that total annual plant cost involved is optimized. The increasing need for dehydrated products of the highest quality, imposes the development of new criteria that, together with cost, determine the design rules for drying processes. Quality of dehydrated products is a complex resultant of properties characterizing the final products, where the most important one is color. Color is determined as a three-parameter resultant, whose values for products undergone drying should deviate from the corresponding ones of natural products, as little as possible. In this case, product quality dryer design is a complex multi-objective optimization problem, involving the color deviation vector as an objective function and as constraints the ones deriving from the process mathematical model. The mathematical model of the dryer was developed and the fundamental color deterioration laws were determined for the drying process. Non-preference multi-criteria optimization methods were used and the Pareto-optimal set of efficient solutions was evaluated. An example covering the drying of sliced potato was included to demonstrate the performance of the design procedure, as well as the effectiveness of the proposed approach.

PRODUCT QUALITY MULTI-OBJECTIVE DRYER DESIGN

ABSTRACT Design of conveyor-belt dryers constitutes a mathematical programming problem involving the evaluation of appropriate structural and operational process variables so that total annual plant cost involved is optimized. The increasing need for dehydrated products of the highest quality, imposes the development of new criteria that, together with cost, determine the design rules for drying processes. Quality of dehydrated products is a complex resultant of properties characterizing the final products, where the most important one is color. Color is determined as a three-parameter resultant, whose values for products undergone drying should deviate from the corresponding ones of natural products, as little as possible. In this case, product quality dryer design is a complex multi-objective optimization problem, involving the color deviation vector as an objective function and as constraints the ones deriving from the process mathematical model. The mathematical model of the dryer was developed and the fundamental color deterioration laws were determined for the drying process. Non-preference multi-criteria optimization methods were used and the Pareto-optimal set of efficient solutions was evaluated. An example covering the drying of sliced potato was included to demonstrate the performance of the design procedure, as well as the effectiveness of the proposed approach.

Application of a novel approach for DMC predictive controller design by response surface analysis in a fed-batch bioreactor

This work presents a novel approach for tuning a predictive DMC controller implemented in a fed-batch penicillin bioreactor in order to stabilize the dissolved oxygen concentration by agitation speed manipulation. The operating process variables were calculated by a deterministic and non-structured model solved by a fourth order Runge-Kutta-Gill numerical technique with variable steps. The parameters of the model were obtained from experiments and the literature. The estimated parameters of the DMC controller were model, prediction and control horizons, suppression factor and reference trajectory. The tuning approach employed complete factorial design in order to estimate the influence of these parameters on the integral of the absolute error between the controlled variable and the set point. Response surface analysis provided the optimal parameters. This study showed negligible influence of model, prediction and control horizons while the suppression factor and reference trajectory were very important for the controller. Another important feature of the DMC controller was the that the parameters had negligible influence on each other making design of the controller easier. The performance of the DMC controller was evaluated using several delay times and sample periods of the controlled variable. The behavior of this predictive controller was better than a PID controller tuned by the Modified Simplex method.