Towards a Structured Framework for Techno-Economic Analyses of Chemical Recycling Technologies

The role of chemical recycling (CR) as a valuable complementary strategy to mechanical recycling in closing the carbon cycle for carbon-containing waste is currently being discussed in political, economic, and social spheres. However, CR deployment is hindered by uncertainties regarding its environmental impacts and costs compared to conventional waste treatment and chemical production routes. While methods for assessing CR's environmental impacts are the focus of socio-political debates and investigations, techno-economic analyses (TEA) to evaluate costs of CR remain scarce. To contribute to a standardized framework for assessing the economic viability of CR technologies, this article draws on life cycle assessment and TEA literature to develop a six-stage TEA process for CR. A checklist is also presented to support transparent and comprehensive analyses.     

Comparative assessment of the dynamic behaviour of an exothermal chemical reaction including data uncertainties

The prediction of the dynamic behaviour of a chemical process is important for reactor design, optimization and safety. It is, however, beset with uncertainties of both, models and their input data. The latter are addressed here and the influence of uncertainties of key parameters, i.e. the heat of reaction, the reaction rate constant and the apparent energy of activation, on the calculation results and the conclusions drawn from them is shown. The conventional approach for the propagation of uncertainties through calculations, the Monte-Carlo method, is compared with calculations using polynomial chaos. The latter require considerably less time for calculation and are hence better suited for parameter variations, which are always needed in the design process. Both approaches are applied to an existing plant for manufacturing the explosive hexogen and illustrated by showing the evolution of the concentration of the product with time and the associated uncertainties. The ranges of predicted production quantities and raw material consumption as well as the impact of uncertainties on designing the dumping system for preventing a runaway reaction after cooling failure are also presented.     

The inverse problem in granulation modeling—Two different statistical approaches

This article is concerned with parameter estimation for a multidimensional population balance model for granulation. Experimental results were obtained by running a laboratory mixer with sodium carbonate and aqueous polyethylene glycol solutions. Subsequently, a prescan of suitable parameter combinations utilising the experimental results is performed, and a local surrogate model constructed around the best combination. For the actual estimation of the parameters and their uncertainties two different approaches are applied—a projection method and a Bayesian approach. It is found that the model predictions with the parameters obtained through both methods are similar. Furthermore, the uncertainties in the model predictions increase as the experimental uncertainties are increased. Studies of the marginal densities of two‐parameter combinations obtained through the Bayesian approach show a correlation between the collision and breakage rate constant, giving potential hints for further model development. Furthermore, a bimodal distribution of the compaction rate constant is observed. © 2011 American Institute of Chemical Engineers AIChE J, 2011     

Robust and optimal control approach for exothermic reactor stabilization

In this paper, the possibility to stabilise open-loop unstable exothermic reactors with temperature measurements is studied. Moreover, the reactors are considered to be multi-input multi-output systems with parametric uncertainties. Robust static output feedback and optimal controllers are designed for stabilization of the exothermic reactors into their open-loop unstable steady states. Stabilization of reactors is simulated using designed controllers. The possibility of using both types of controllers for energy savings is studied and measured by coolant consumption. The approach is tested with a representative example. Obtained simulations results confirm that the robust static output feedback controllers outperform optimal controllers when systems with parametric uncertainties are controlled.     

Geometrical alignment errors in the measurement of prismatic retroreflectors

Measurements of the coefficient of luminous intensity R for prismatic retroreflectors contain errors due to misalignment of the photometric range and samples. A procedure for assessing these errors has been developed for the two-orientation hexagon-cube clear prismatic retrore-flector that is included in the NBS Measurement Assurance Program (MAP) for retroreflection. This procedure involves experiments in which the geometrical setting of the range is deliberately varied so that the dependence of R on small variations in the geometry can be estimated. These experiments followed a fractional factorial design. Bounds on the uncertainties in the observation angle α, the two components of the entrance angle β₁ and β₂, and the rotation angle ? can be used to bound the corresponding uncertainty in R due to geometrical effects. Results of this experiment are reported along with some repeated measurements of R for an internal NBS MAP. Similar coefficients have been reported previously for retroreflective bead sheeting, and their implication is discussed.     

Adsorption of methane, nitrogen, carbon dioxide and their mixtures on wet Tiffany coal

Gas adsorption was measured for methane, nitrogen, CO₂ and their binary and ternary mixtures on a wet Tiffany coal sample. The measurements were conducted at 327.6 K (130.0 F) at pressures to 13.8 MPa (2000 psia). The expected uncertainties in the amounts adsorbed vary with pressure and composition. In general, average uncertainties are about 5% (0.01–0.08 mmol/g) for the total adsorption; however, the expected percentage uncertainties in the amount of individual-component adsorption are significantly higher for the lesser-adsorbed gas at lower molar feed concentrations (e.g. nitrogen in the 20/80 nitrogen/CO₂ system).

The Langmuir/loading ratio correlation (LRC) and the Zhou–Gasem–Robinson (ZGR) two-dimensional equation of state (EOS) are capable of representing the total adsorption for the pure, binary and ternary systems within their expected experimental uncertainties. However, the quality of fit for the individual-component adsorption varies significantly, ranging from 3% (0.01 mmol/g) for the more-adsorbed methane or CO₂ to 32% (0.01 mmol/g) for the lesser-adsorbed nitrogen. Further, the LRC and ZGR EOS predict binary adsorption isotherms, based solely on pure-fluid adsorption parameters, within twice their experimental uncertainties.     

Experimental Diagnosis on Frictional Initiation of a Match Head Composition at Different Contact Materials

The mechanism of initiation is essential to enumerate the sensitivity and evaluate the risks associated with energetic mixtures. To understand the mechanism of initiation under frictional loading, the initiation events such as initiating time and critical load have to be evaluated. This study aims to assess experimentally the initiation mechanism under frictional load by measuring the event of initiation and the critical load using a modified version of the BAM friction tester augmented with a press force sensor, an accelerometer and a photodiode arrangement. Differences in the range of diagnostics were identified for various material interactions. The data generated have the implications for assessing the safety of energetic mixtures.     

UXO and environmental risk factors impacting EOD operations in German waters**

This article presents risk factors that are associated with the handling of unexploded ordnance (UXO) during explosive ordnance disposal (EOD) operations in German waters. The construction of offshore wind parks and the German immediate action program are expected to increase the number of EOD operations. Existing literature and guidelines do not offer a structured and reproducible framework for assessing EOD risk. To fill this gap, a network of EOD risk factors was developed by means of a literature review and validation via expert consultation. The study was scoped to “personnel and equipment at the EOD location” as the risk receptor and “undesired detonation” as the undesired event under investigation. Factors are subdivided into UXO factors that depend on the object that should be handled and factors that describe the object's surrounding environment. While the former can be researched by an EOD expert, the latter must be measured on site or acquired from a model. Each of these factors contributes to risk, some directly and others indirectly via other factors. The complexity of the resulting network, with its 33 factors, demonstrates the need for a reliable and reproducible model to quantify EOD risk. Its purpose is not to replace EOD experts but to aid them in their decision-making process. Such a tool can provide valuable support for the high-cost and high-risk EOD operations.     

Incorporating experimental uncertainties into multivariate granulation modelling

A methodology that carries experimental uncertainties into model predictions is studied and applied to a multidimensional population balance model for granulation processes. This complex model contains 27 parameters. A portion of them such as material constants can be measured or estimated, whereas some of the model parameters need to be established through granulation experiments and subsequent fitting to the model. As uncertainties are associated with every measurement, these are used in the presented methodology for the computation of uncertainties in the model predictions. This allows one to assess the quality of a model and to identify outliers in the experimental observations. As the evaluation of the complex model framework is computationally expensive, the granulation process is approximated with response surfaces in the studied example, allowing the quick computation of the model response in the optimisation procedure. Using eight sets of experimental observations, model-specific rate constants for particle coalescence, compaction, breakage, and reaction are calculated. Additionally, uncertainties of these parameters are estimated, allowing for the calculation of the model prediction and its uncertainty. Whereas the a priori uncertainties are relatively large, the uncertainties are significantly reduced by the method proposed. In addition to this, a possible mismatch between the model and the experimental observations is identified, giving hints for further investigations.     

Probability bounds analysis for nonlinear dynamic process models

Dynamic process models frequently involve uncertain parameters and inputs. Propagating these uncertainties rigorously through a mathematical model to determine their effect on system states and outputs is a challenging problem. In this work, we describe a new approach, based on the use of Taylor model methods, for the rigorous propagation of uncertainties through nonlinear systems of ordinary differential equations (ODEs). We concentrate on uncertainties whose distribution is not known precisely, but can be bounded by a probability box (p‐box), and show how to use p‐boxes in the context of Taylor models. This allows us to obtain p‐box representations of the uncertainties in the state variable outputs of a nonlinear ODE model. Examples having two to three uncertain parameters or initial states and focused on reaction process dynamics are used to demonstrate the potential of this approach. Using this method, rigorous probability bounds can be determined at a computational cost that is significantly less than that required by Monte Carlo analysis. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

Accurate determination of fiber strength distributions

Tensile strengths of small‐diameter ceramic fibers are commonly obtained from measured fracture loads on individual fibers and the average cross‐sectional area of the entire fiber population. The goal of the present article is to provide a critical assessment of the consequences of using the average fiber area in the inferred strength distribution. The issues are addressed through established theorems in convolution and uncertainty propagation as well as Monte Carlo simulations. Systematic errors introduced by using the average area are well‐represented by simple analytical formulae. The formulae are couched in terms of the coefficient of variation in fiber area and the dispersion in fiber strengths, characterized by the Weibull modulus. In turn, the formulae are used to determine the true values of Weibull modulus and reference strength from their nominal values. Random uncertainties associated with a finite number of tests decay slowly with number, in accordance with an inverse root scaling. When systematic errors are conflated with random uncertainties, accurate determination of the true Weibull modulus becomes increasingly challenging, even for seemingly large numbers of strength measurements. The results are used to assess the fidelity of previously‐reported experimental results based on nominal strength data.     

Robust identification and control of batch processes

An approach is proposed for the robust identification and control of batch and semibatch processes. The batch experiments used for model identification are designed by minimizing the magnitude of the parameter uncertainties, and the effect of these uncertainties on the product quality achievable by optimal control is used as a stopping criterion for the identification procedure. The optimal control approach incorporates a quantification of the impact of both parameter and control implementation uncertainties on the performance of the optimal control policy. The approach is applied to the nucleation and growth of crystals with multiple characteristic dimensions, where the nominal parameters used in the simulation study are quantified from experimental data.     

Parameter estimation and prediction uncertainties for multi-response kinetic models with uncertain inputs

Error-in-variables model (EVM) methods are used for parameter estimation when independent variables are uncertain. During EVM parameter estimation, output measurement variances are required as weighting factors in the objective function. These variances can be estimated based on data from replicate experiments. However, conducting replicates is complicated when independent variables are uncertain. Instead, pseudo-replicate runs may be performed where the target values of inputs for repeated runs are the same, but the true input values may be different. Here, we propose a method to estimate output-measurement variances for use in multivariate EVM estimation problems, based on pseudo-replicate data. We also propose a bootstrap technique for quantifying uncertainties in resulting parameter estimates and model predictions. The methods are illustrated using a case study involving n-hexane hydroisomerization in a well-mixed reactor. Case-study results reveal that assumptions about input uncertainties can have important influences on parameter estimates, model predictions and their confidence intervals.     

An innovative bed temperature-oriented modeling and robust control of a circulating fluidized bed combustor

Circulating fluidized bed (CFB) combustion systems are increasingly used as superior coal burning systems in power generation due to their higher efficiency and lower emissions. However, because of their non-linearity and complex behavior, it is difficult to build a comprehensive model that incorporates all the system dynamics. In this paper, a mathematical model of the circulating fluidized bed combustion system based on mass and energy conservation equations was successfully extracted. Using these correlations, a state space dynamical model oriented to bed temperature has been obtained based on subspace method. Bed temperature, which influences boiler overall efficiency and the rate of pollutants emission, is one of the most significant parameters in the operation of these types of systems. Having dynamic and parametric uncertainties in the model, a robust control algorithm based on linear matrix inequalities (LMI) have been applied to control the bed temperature by input parameters, i.e. coal feed rate and fluidization velocity. The controller proposed properly sets the temperature to our desired range with a minimum tracking error and minimizes the sensitivity of the closed-loop system to disturbances caused by uncertainties such as change in feeding coal, while the settling time of the system is significantly decreased.     

Assessment of Color-Measuring Instruments

The U.S. Army has undertaken a program to develop an instrumental method for assessing the acceptability of textiles for color difference from a standard. This article reports the results of the first phase of the program, an assessment of three commercial color-measuring instruments (Diano Match-Scan, Hunter D-54P-5, Macbeth MS-2000) for objective textile acceptability judgment. It is concluded that the three instruments are essentially equivalent in the precision and accuracy of the measurement of color and color difference using a wide variety of samples, at least over periods of up to seven weeks. All measures of repeatability lead to the conclusion that the uncertainties involved are well below the just-perceptible color difference. Levels of absolute accuracy achieved depend greatly on the details of operation, data treatment, and calibration, but are considered satisfactory for each of the instruments tested. With respect to certain other parameters, the performance of the instruments is less satisfactory. Rejection of the specular component differs significantly among the three, as does the selection of weights for tristimuus calculations. One instrument, as tested, exhibited significant sensitivity to weave orientation in textile samples. Finally, we find that the distributions of tristimulus values obtained with each of the instruments show large deviations from normality, severely limiting the significance of conventional statistical treatment of such data.     

Optimal processing pathway selection for microalgae-based biorefinery under uncertainty

We propose a systematic framework for the selection of optimal processing pathways for a microalgae-based biorefinery under techno-economic uncertainty. The proposed framework promotes robust decision making by taking into account the uncertainties that arise due to inconsistencies among and shortage in the available technical information. A stochastic mixed integer nonlinear programming (sMINLP) problem is formulated for determining the optimal biorefinery configurations based on a superstructure model where parameter uncertainties are modeled and included as sampled scenarios. The solution to the sMINLP problem determines the processing technologies, material flows, and product portfolio that are optimal with respect to all the sampled scenarios. The developed framework is implemented and tested on a specific case study. The optimal processing pathways selected with and without the accounting of uncertainty are compared with respect to different objectives.     

Development of a risk assessment program for chemical terrorism

The study focuses on assessing the security risk of the terrorism in the chemical industry. This research modifies conventional risk assessment methods for including terrorism and sabotage scenarios. The objective of this risk assessment is to identify security hazards, threats and vulnerabilities facing each target facility, and to find the adequate countermeasures to protect the public, workers, national interest, environment, and companies. This study results in implementing software to analyze the possibility of terrorism and sabotage. This program includes five steps: asset characterization, threat assessment, vulnerability analysis, risk assessment and new countermeasures. It is a systematic, risk-based approach in which risk is a function of the severity of consequences of an undesired event, the likelihood of adversary attack, and the likelihood of adversary success in causing the undesired event. The reliability of this method is verified by the dock zone case. This study suggests an effective approach to chemical terrorism response management.     

Reduction of greenhouse gas emissions via flexible operation of cross-sector integrated energy systems under uncertainties in demand,fuel prices,and solar irradiation

This work investigates how the flexible operation of the light industrial plants integrated in a cross-sector energy cluster with community energy systems can achieve further greenhouse gas (GHG) reductions under uncertainties associated with natural gas prices, solar irradiation, as well as heating, cooling, and electricity demand. The optimal flexible operation and design of a cross-sector integrated cluster comprising a bakery plant, a brewery, a confectionery plant, a residential building, and a supermarket under uncertainties are compared to the operation and design of systems without uncertainties. When uncertainties are considered, the overall GHG emissions of the integrated system with steady industrial production rates for all uncertainty scenarios are over 4% higher than the integrated system in the deterministic scenario (a single scenario). Flexible operation of the industrial plants, whereby production rates are varied throughout the day, contributes an additional 3% reduction in GHG emissions under uncertainties, where the GHG emissions are only 1% higher than the deterministic scenario. Additionally, the system with flexible production rates purchases over 14.3% less electricity from the grid and uses over 72.2% less natural gas for operating the backup boiler, which relies less on supplementary energy resources. This shows that optimally designed integrated systems with flexible industry production schedules are resilient to uncertainties in energy demands, daily weather fluctuations, and fuel prices.     

A new approach for scheduling of multipurpose batch processes with unlimited intermediate storage policy

The increasing demand of goods, the high competitiveness in the global marketplace as well as the need to minimize the ecological footprint lead multipurpose batch process industries to seek ways to maximize their productivity with a simultaneous reduction of raw materials and utility consumption and efficient use of processing units. Optimal scheduling of their processes can lead facilities towards this direction. Although a great number of mathematical models have been developed for such scheduling, they may still lead to large model sizes and computational time. In this work, we develop two novel mathematical models using the unit-specific event-based modelling approach in which consumption and production tasks related to the same states are allowed to take place at the same event points. The computational results demonstrate that both proposed mathematical models reduce the number of event points required. The proposed unit-specific event-based model is the most efficient since it both requires a smaller number of event points and significantly less computational time in most cases especially for those examples which are computationally expensive from existing models.