A conceptual model for integrating sustainable supply chain, electric vehicles, and renewable energy sources

The effects of climate change can be seen immediately in ecosystems. Recent events have resulted in a commitment to the Paris Agreement for the reduction of carbon emissions by a significant amount by the year 2030. Rapid urbanisation is taking place to provide room for an increasing number of people’s residences. Increasing the size of a city and the number of people living there creates a daily need for consumable resources. In the areas of transportation, supply chains, and the utilisation of renewable energy sources, deliver on pledges that promote the accomplishment of the Sustainable Development Goals established by the United Nations. As a result, the supply chain needs to be handled effectively to meet the requirements of growing cities. Management of the supply chain should be in harmony with the environment; nevertheless, the question of how to manage a sustainable supply chain without having an impact on the environment is still mostly understood. The purpose of this study is to present a conceptual model that may be used to maintain a sustainable supply chain with electric vehicles in such a way that caters to both environmental concerns and human requirements. As part of the continual process of achieving sustainability, interrelationships between the various aspects that are being investigated, comprehended, and applied are provided by the model that was developed. It is self-evident that governmental and international organisations that are concerned with supply-demand side information will benefit from such a model, and these organisations will locate viable solutions in accordance with the model’s recommendations. Beneficiaries consist of individuals who are active in the supply chain and are concerned with supply-demand side information. These individuals also need to understand how to effectively manage this information

Performance analysis of IMC based PID controller tuning on approximated process model

Classical Proportional Integral Derivative(PID) controller remains the most popular approach for industrial process control. Poor tuning of PID controller can lead to mechanical wear associated with excessive control activity, poor control performance and even poor quality products. In this paper, we design procedure for the internal model control(IMC) approach for tuning of conventional PID controller with proper tuning rules. Furthermore, with help of analytical rule of step test obtaining the effective first order time delay model of the process. A simulation example of continuous stirred tank reactor is used in which the IMC based PID tuning method implemented and the step response of the closed loop system is compared with classical tuning methods like Ziegler-Nichols and Cohen-Coon

Multi-criterion control of a bioprocess in fed-batch reactor using EKF based economic model predictive control

This research paper presents an offline and online user defined priority driven multi-objective optimal control study of a bioprocess in a fed-batch reactor. Productivity and the amount of substrates used in the process are considered as the two control objectives in that order of priority for this purpose. The priorities in the objective functions are realized using the lexicographic approach by sequentially solving multiple objectives to arrive at a Pareto solution point. This approach is not sensitive to the tuning of weighting parameters as compared to the scalarized objective, practiced conventionally. The weighting factors tuning issue is demonstrated with an offline optimal control. The lexicographic optimization approach is then implemented to overcome this thing issue. Subsequently, the online optimal control problem is solved using economic model predictive control (EMPC) owing to the economic nature of the control objectives. Often, the Pareto curve is such that marginally relaxing one objective results into a significant improvement in the other objective. This can easily be implemented with the lexicographic approach and is demonstrated using EMPC. Moreover, unlike the continuous processes, the batch processes operate for a specific batch time. Hence, the shrinking horizon approach along with the EMPC framework is employed in the fed-batch bioreactor for online control with extended Kalman filter (EKF).by Markana Anilkumar, Nitin Padhiyar and Kannan Moudgaly

Multi-objective optimization based optimal sizing & placement of multiple distributed generators for distribution network performance improvement

Integration of Distributed Generations (DGs) into radial distribution network (RDN) is an emerging need to explore the benefits of renewable energy sources (RES). Increasing penetration of RES based DGs in RDN without proper planning leads to several operational problems such as excessive energy losses, poor voltage quality and load balancing. Hence, in this work, multi-objective optimization (MOO) problem is formulated by carefully chosen three conflicting objectives such as power loss minimization, enhancement of load balancing index (LBI) and aggregate voltage deviation index (AVDI). Teaching-Learning-Based-Optimization (TLBO) is used to optimize MOO problem considering placement of DGs at multiple locations in RDN satisfying the constraints on bus voltage magnitude, branch flows and DG size. Comprehensive simulation studies have been carried out to obtain optimal performance for 69-nodes RDN with the increasing penetration of DGs at multiple locations. It is shown that determination of optimal sizing of DGs at multiple locations in RDN with MOO results in lesser power losses, improved voltage profiles and better load balancing as compared to placement of single DG in RDN. Performance measures such as spacing and spread indicators are used for characterizing Pareto solutions for MOO problem. Such set of non-dominated solutions obtained from Pareto front during multi-objective TLBO gives proper guidelines to the utility operator about sizing and placement of DGs based on the assigned priorities to the objectives

Multi-objective prioritized control of a semi-batch process with multiple feed and multiple products using economic MPC

by Markana Anilkumar, Nitin Padhiyar, and Kannan Moudgaly

Multi-objective control of a fed-batch bioreactor using shrinking horizon MPC: a case study

by Anilkumar Markana, Nitin Padhiyar and Kannan Moudgaly

Lexicographic optimization based MPC: simulation and experimental study

Multi-variable prioritized control study is carried out using model predictive control (MPC) algorithms. The conventional MPC algorithm implements multi-variable control through one augmented objective function and requires weights adjustment for required performance. In order to implement explicit prioritization in multiple control objectives, we have used lexicographic MPC. To achieve better tracking performance, we have used a new MPC algorithm, by modifying the lexicographic constraint, referred to as MLMPC, where tuning of weights is not required. The effectiveness of MLMPC algorithm is demonstrated on a PMMA reactor for controlling the number average molecular weight and the reactor temperature. We have also verified the benefits of proposed algorithm on an experimental single board heater system (SBHS) for controlling temperature of a thin metal plate. These simulation and experimental studies demonstrate the superiority of the proposed method over conventional MPC and lexicographic MPC. Finally, we have presented generalized mathematical solutions to the optimization problem in MLMPCby Nitin Padhiyarby Markana Anilkumar, Nitin Padhiyar and Kannan Moudgalya

Prioritized control of multivariate process using lexicographic ordering approach: a simulation study

Design of a multivariate control system is a challenging task owing to inherent process nonlinearities, multivariable interactions, and unstable zeros in the process dynamics. Prioritization of multiple control objectives is conventionally achieved using scalarization approach by appropriate weighting factors in the augmented objective function. However, the optimality of these parameters does not hold in presence of unmeasured disturbance and different setpoints. Hence, prioritization in the objectives is not achieved in such circumstances. On the other hand, priority in various objectives can effectively be realized in lexicographic optimization approach. We use the lexicographic optimization approach to address setpoint tracking control problem in linear MPC. To validate the efficacy of the lexicographic ordering approach, multivariate quadruple tank process is considered. Tuning issues with conventional MPC and its remedies using lexicographic approach are discussed in the present work.by Markana Anilkumar, Nitin Padhiyar and Kannan Moudgaly