An order-specific clustering algorithm for the determination of representative demand curves

Data clustering consists of a group of procedures used to collect similar entries or data points within a set into clusters. No existing clustering technique considers entries sequentially in time. In some cases, it is desirable to generate clusters that represent a segment of a time-ordered data set. For these purposes, an order-specific clustering algorithm is proposed. The proposed algorithm employs representative load curves to describe the clusters it generates. The capabilities of the order-specific clustering algorithm are demonstrated on a case study using electricity demand data for the province of Ontario, Canada. Two different applications of the clustering algorithm on this data set are given to demonstrate the effect of error threshold values on the formation of clusters. An analysis of the error for each of these clustering applications is presented.     

Multivariate Modeling of a Chemical Toner Manufacturing Process

Modeling, optimization, process monitoring, and product development in a toner process using multiway principal component analysis and multiway partial least square method is described. Process measurements and product quality values of past successful batches were collected in a data matrix and preprocessed through time alignment, centering, and scaling. Following the identification of latent variables, an empirical model was built through a fourfold cross validation that can represent the operation of a successful batch. The prepared model provided a realistic prediction of process behavior, realistically represented the operation of the industrial unit, and is mathematically simple enough to be used in online optimization and for automatic control strategies of selected abnormal batches.     

Using short‐term resource scheduling for assessing effectiveness of CCS within electricity generation subsector

A new methodology for assessing the effectiveness of carbon capture and storage (CCS) that does explicitly consider the detailed operation of the target electricity system is proposed. The electricity system simulation consists of three phases, each one using a modified version of an economic dispatch problem that seeks to maximize the producers’ and consumers’ surplus while satisfying the technical constraints of the system. The economic dispatch is formulated as a dynamic mixed‐integer nonlinear programming problem and implemented in general algebraic modelling system (GAMS). The generating unit with CCS is designed and simulated using Aspen Plus®. In the first case study, the operation of the IEEE RTS ’96 (Institute of Electrical and Electronics Engineers One‐Area Reliability Test System—1996) is simulated with greenhouse gas (GHG) regulation implemented in the form of CO₂ permits that generators need to acquire for every unit of CO₂ that it is emitted. In the second case study, CCS is added at one of the buses and the operation of the modified IEEE RTS ’96 is again simulated with and without GHG regulation. The results suggest that the detailed operation of the target electricity system should be considered in future assessments of CCS and a general procedure for undertaking this for any GHG mitigation option is proposed. Future work will use the novel methodology for assessing the effectiveness of generating units with flexible CO₂ capture. © 2015 American Institute of Chemical Engineers AIChE J, 61: 4210–4234, 2015     

Dynamic population balance and flow models for granular solids in a linear vibrating screen

Vibrating screens are a widely applied form of particle separations. In spite of this significance, their understanding is still an obstacle. Three approaches were used to characterize the flow of granular material in a linear vibrating screen. The statistical model, mass action, and kinetic model based on conservation of momentum were derived. Experiments were then conducted on a multi‐sized prototype screen and glass beads of sizes 0.75, 1, 2, 3 mm. Deck inclinations were varied over 7.5,12.5, and 17.5°, and frequencies over 7, 15, and 20 Hz. A total of 72 feed batches and a constant power of 50 W was used. The experimental data was then used to validate the models. The three models provided accurate flow prediction over the screens. Additionally, the kinetic model also provided a basis for optimal design of the screening unit operation, by allowing manipulation of seven design variables to obtain a 95–100% efficient vibrating screen. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3889–3898, 2016     

Asphaltene precipitation and deposition in oil reservoirs – Technical aspects,experimental and hybrid neural network predictive tools

Precipitation of asphaltene is considered as an undesired process during oil production via natural depletion and gas injection as it blocks the pore space and reduces the oil flow rate. In addition, it lessens the efficiency of the gas injection into oil reservoirs. This paper presents static and dynamic experiments conducted to investigate the effects of temperature, pressure, pressure drop, dilution ratio, and mixture compositions on asphaltene precipitation and deposition. Important technical aspects of asphaltene precipitation such as equation of state, analysis tools, and predictive methods are also discussed. Different methodologies to analyze asphaltene precipitation are reviewed, as well. Artificial neural networks (ANNs) joined with imperialist competitive algorithm (ICA) and particle swarm optimization (PSO) are employed to approximate asphaltene precipitation and deposition with and without CO₂ injection. The connectionist model is built based on experimental data covering wide ranges of process and thermodynamic conditions. A good match was obtained between the real data and the model predictions. Temperature and pressure drop have the highest influence on asphaltene deposition during dynamic tests. ICA-ANN attains more reliable outputs compared with PSO-ANN, the conventional ANN, and scaling models. In addition, high pressure microscopy (HPM) technique leads to more accurate results compared with quantitative methods when studying asphaltene precipitation.     

Sustainability indicators for decision-making and optimisation in the process industry: The case of the petrochemical industry

This paper brings into focus simple sustainable development indicators to be used in the planning of the petrochemical industry. The indicators cover the three aspects of sustainability—environmental, economic and safety. The indicators serve as tools that can guide companies in assessing their performance in decision-making and planning for the future. These simple indicators of sustainable development are especially helpful in planning large projects or during the earliest stages of planning when most detailed process information is still lacking. These sustainability indicators were used as objectives for a mixed-integer optimisation model to plan the development of a typical petrochemical industry, and were found to be very useful in identifying a balanced petrochemical network. A simple Monte Carlo simulation showed that the model is able to accommodate variations in prices and demand.     

Optimization of the Cementation Process for the Removal of Copper in Process Effluent Discharges

Cementation is an effective process that meets the requirements of process cleanliness. The performance of the cementation reaction is dictated by a number of variables. In this study, the effects of various parameters on the yields of copper‐iron cementation (wire and powder) were investigated. Statistical full factorial designs were used to produce appropriate mixture models for the yields. The experimental designs were done at three different levels of three operating variables, namely initial concentration, temperature, and pH. The optimization studies were carried out with MATLAB 7.0 software. The optimum yields obtained from the predicted models were found to be 0.9916 and 0.9088 for iron wire and powder, respectively, under optimized conditions, and these were in accordance with the experimental observations.     

Experimental investigation of the catalytic cracking of methane over a supported Ni catalyst

TGA experiments on the catalytic cracking of methane and methane diluted with hydrogen over a 5 wt% Ni/γ‐alumina catalyst at 500°C and 1 and 10 bar are described. Carbon deposition on the catalyst, as expressed in specific weight of carbon, varies with time. Eventually, the deposition stops because of deactivation. The specific weight at this point depends on the hydrogen partial pressure and there is also a weak dependence on the degree of contact between catalyst particles. A model based on assuming carbon deposition proceeds through whisker formation and growth, and deactivation occurs through encapsulation adequately describes the experimental observations up to 20 vol% hydrogen in the feed.