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Modeling, Simulation, and Optimization in the Process and Commodities Industries
Published in Mariano Martín Martín, Introduction to Software for Chemical Engineers, 2019
Iiro Harjunkoski, Mariano Martín
Technologies that have established themselves also within the industrial applications are for instance linear MPC and especially owing to the good developments of commercial solvers (e.g., CPLEX®, Gurobi®, XPRESS-MP®) and commercial modeling environments (e.g., MATLAB®, GAMS®, AIMMS®, AMPL®, MOSEK®) many mathematical programming approaches have been widely implemented to tackle problems within production planning and optimization problems. Looking back in the history, it is not unusual that from the first concept introduction it takes 20–40 years before a solution technology becomes a commodity in industrial practice. Thus it is valid to expect that many of the novel approaches today will be established as widely accepted industrial solutions after 2020.
Introduction
Published in Sugato Basu, Ian Davidson, Kiri L. Wagstaff, Constrained Clustering, 2008
Sugato Basu, Ian Davidson, Kiri L. Wagstaff
The second set of experiments was run over a higher-dimensional data set derived from web-browsing behavior to a large internet portal. The browsing history for a group of 10,144 randomly selected users to 300 of the most popular news category stories was generated. This data set can be viewed as 10,144 data points in R300. We refer to this data set as the "Web Data Set." In order to handle this larger data set, we modify our original MATLAB code and utilize MOSEK 4.0 as the linear programming solver [18], which can be seamlessly integrated with MATLAB.
Introduction to constraint-based modelling
Published in Karthik Raman, An Introduction to Computational Systems Biology, 2021
All the above tools rely on at least one of the solvers for LP/QP/MILP, such as IBM CPLEX (https://www.ibm.com/analytics/cplex-optimizer), GLPK (https://www.gnu.org/software/glpk/), Gurobi (https://www.gurobi.com/), MOSEK (https://www.mosek.com/), PDCO (https://web.stanford.edu/group/SOL/software/pdco/), and Tomlab CPLEX (https://tomopt.com/tomlab/products/cplex/), or even MATLAB linprog. More details can be found in Appendix D.
Impact analysis of demand response on optimal allocation of wind and solar based distributed generations in distribution system
Published in Energy Sources, Part B: Economics, Planning, and Policy, 2021
Tanuj Rawat, K. R. Niazi, Nikhil Gupta, Sachin Sharma
In recent years there is a growing trend to employ DR to leverage the benefits of flexibility of customers demand to improve the performance of distribution network. In this paper, a framework for sizing and siting of wind and solar-based DGs in distribution system in coordination with DR is developed and investigated by formulating the problem as a MISOCP to minimize energy loss. The commercially available MOSEK solver is used to solve the optimization problem. Various cases are simulated and compared in terms of energy loss, minimum voltage, average voltage deviation, penetration level of DGs and peak demand of upstream grid to show the impact of DR on optimal planning of DGs. Impact of varying DR participation rates and maximum number of locations where DGs can be located is examined. The investigation results show that the proposed MISOCP-based approach is better than most of the available meta-heuristic approaches and is at par with the best-reported method. The simulation results show that integrating DR with planning of DGs leads to more energy-saving and improvement in voltage profile. These impressive benefits are paired with increase in penetration of renewables in distribution system. It is observed that joint allocation of WT and PV will minimize energy losses significantly as compared to allocation of WT/PV alone. Further, it is seen that, technical parameters of the distribution network enhance as DR participation rate increases. Impact of network reconfiguration and uncertainty associated with PV and WT on these results can be explored as part of future research.
Robust model predictive control of HVAC systems with uncertainty in building parameters using linear matrix inequalities
Published in Advances in Building Energy Research, 2020
Himanshu Nagpal, Andrea Staino, Biswajit Basu
The model of building climate described in section 3 is implemented in Matlab. The LMIs in optimization problem is formulated using modelling toolbox YALMIP (Löfberg, 2004). The whole set-up is simulated for a one week period (168 h) with a sample time of 30 min. The ambient temperature and solar irradiance are obtained from ASHRAE IWEC (International Weather for Energy-Calculations) weather data files for Scotland (2017) and presented in Figure 1. Further, this data is extrapolated using a sample time of 30 min to comply with the sampling time used for discretization of the continuous-time system model (1). In the real-time implementation of the proposed controller, this historical data can be replaced by the weather forecast. At each time step, the optimization problem (17) is solved using MOSEK Solver integrated with Matlab.
A simple effective heuristic for embedded mixed-integer quadratic programming
Published in International Journal of Control, 2020
Reza Takapoui, Nicholas Moehle, Stephen Boyd, Alberto Bemporad
Comparison of the runtime with commercial solvers such as MOSEK (ApS, 2015) and CPLEX (CPLEX, 2009) shows that our method can be substantially faster than solving a global optimisation method, while having a competitive practical performance.