FIELD OF INVENTION:
The invention relates to control of gas flows in pipelines in general and to
optimization of gas flow in pipelines at steel plants in particular.
BACKGROUND OF PRIOR ART:
KEY WORDS
Energy Management: Management of the gas flows in the pipeline network
NM^3/Hr: Normal Meter Cubed per Hour: Used as a measure of gas flow
MWC: Meters of water Column Absolute: Used as a measure of the gas pressure:
Atmospheric pressure lOMWC
THM: Tonnes of Liquid steel
BF: Blast Furnace
BF gas: Blast Furnace gas
CO gas: Coke Ovens gas
CV: Calorific Value of a fuel gas [Kcal/NM^3]
BOF shop: Oxygen Furnace shop
LD gas: Gases from the LD converters in the BOF shops
GCP: Gas cleaning plant at the Blast Furnace
BLEEDER: For flaring gases at Blast Furnace
STV: Stove at Blast Furnace
BLR: Blower at Blast Furnace
BAT: Coke Oven Battery
HSM: Hot strip mill
PHS: Power House for electricity generation
GH: Gas Holder
OCSP: Ore Crushing & sintering Plant
SGDP: Slag Granulation & Drying Plant
GUI: graphical User Interface
Tag Name: A unique identifier for an item in the database
The integrated steel making process consists of several plants, converting raw
materials to finished products. The overall steel making process is energy
intensive and consumes elementary inputs like coal, electricity and special gases.

Some of the processes like the blast furnace, coke ovens and steel converter
generate low and medium CV fuel gases as by products. The byproduct fuel
gases are blended and distributed as energy inputs to many plants within the
steel works. The distribution and control is critical to operate the steel works in
and efficient and stable manner.
During power disturbances and operating shutdowns of process plants the gas
generation suffers large disturbance causing a crisis. The crisis and recovery
need special attention of plant managers. The management of the byproduct gas
network is complex and cannot be successfully managed by human interventions
alone. The human responses in a crisis can be slow and inadequate.
OBJECT OF INVENTION:
To overcome the drawbacks of the Prior art innovative remedial actions are
undertaken to improve it. The objectives of this innovation are (a) to design an
energy management system and (b) an expert system to control the by-product
gas network involving the following in consideration.
1. Measure and calculate Generation/Consumption of by-product gases at
relevant points
2. Prognostic determination of demand and generation
3. Gas balancing based on present and future states
4. Monitor special states of the Generation and Consumption points
5. Mapping and deduction of local scenarios at generation and consumption
point based on the special states
6. Simulate the global scenarios based on the local scenarios
7. Adaptive decision tree mechanism for control action based on preset rules
8. Crisis Management (Guidance)
9. Energy Audit

BRIEF DESCRIPTION OF ACCOMPANYING DRAWING :
The invention will now be described with help of the accompanying drawings
which depict an exemplary embodiment of the invention. However, there can
be several other embodiments, all of which are deemed covered by the
description.
Fig.1 shows schematic diagram of production process of entire plant
Fig.2 shows broad structure of the parameters addressed by the system
Fig.3 shows trend diagram of normalized flows for CO & BF gas to the stove of
furnace gas generation in a blast furnace blast
Fig.4 schematic showing the B F gas network
Fig.5 schematic showing the CO gas network
Fig.6 schematic showing the B F gas network flows [NM^3/Hr]
Fig.7 schematic showing the B F gas network Pressure Heads [M Water
Column]
Table # 1: shows Energy Consumption accompanied by bar diagrams of specific
energy Consumption and specific power consumption.
The system has basically a centrally located server, which is connected to a
plurality of individual plant-level control system and has inter and intraface
communication to receive /process/compare/ store/transfer data among each
other and accordingly the system can take corrective measures. The system
described here, takes data from distributed sensors in the plant for processing. It
searches over the possible valve settings and determines optimization. It can
also operate in permissible range and priority as governed by rules embedded in
the module. The system is comprising of various functional modules, like Plant
configuration, gas balance, network simulation, data acquisition, network

diagnostics (reports), energy audit (reports), expert decision, crisis management
etc.
Fig.2, shows the broad structure of parmeters addressed to the system.
The system being in modular form so that deployment in other units of the
enterprise can be done with customization.
The system described in Fig.1, the production processes like the blast furnace
(BF), coke ovens (CO) and steel converter generate low and medium CV fuel gas
as by product. The by-product fuel gases are blended and distributed as energy
inputs to many plants within the steel works efficiently. The subject of this
application is a model, iterative implementation which takes inputs from sensors
at several locations, where the various type of gaseous fuels are generated in a
steel mill like blast furnace (BF) coke oven gases (CO) and the gases that are
generated in the basic oxygen furnace during making of steel (LD) and provides
an out put to the valve actuators, to organize the flow of gas with efficient
energy management. Several consumption centers where the gases are burnt
like blast furnace stove (STV), hot strip mill (HSM), power house for electricity
generation (PHS) (OCSP), (SGDP), storage facility of the gas (GH) and also flow
enhancer (BLR) are considered. Explaining the function of modules, functioning
of a typical Data Acquisition module in control of blast furnace system may be
considered.
The BF-gas requirement from the network to supply the blast furnace stoves till
internal generation from the blast furnace picks up, has to be anticipated from
prior tends, which could be seen in Fig.3. The purpose of smoothened trend [as
shows in Fig.3] provides a better estimate.
Another example, which explains the function of Network simulation module is
stated as under.

The network simulator in the network simulation module provides a complete
analysis of the flow condition in a pipe line network, Fig.4&5, show typical BF
&CO gas network respectively . Fig 6, shows the flows in a network for specific
situation while Fig.7, shows the concomitant pressures. Thus the system runs
efficiently according to an well designed function oriented modular energy
management system, which serves as a means of effective energy utilization
across plants in various utility networks.
Table #1 shows energy consumption supported by bar diagrams of specific
energy consumption & specific power consumption.
DESCRIPTION OF INVENTION:
The system herein described performs a centralized function for the integrated
plant. In derives data from the plant and activates controls. Depending on the
complexity of local operations and safely, certain controls are executed locally at
the locations on advice of the system.
The system can also be suitably linked to associated central systems like the
Power System Manager and the Iron Control System.
The system is developed with tools to configure new features dynamically and
quickly. Changes in the pipeline or the addition of booster pumps can be quickly
incorporated. Thus enhancements and modifications can be accomplished using
these engineering tools. The system is in modular form so that deployment in
other units of the enterprise can be done with customization.
The broad structure of the parameters addressed by the system is shown in
Fig.#2.

The system described here, consist of various modules.
(A) Plant Configuration Module
In this module all screens of energy management system which
comprise a pipeline network and a plant layout are displayed. With this
module a plant manager can modify the pipeline network or alter the
capacities of the utilities (Gas storage capacity, Valves, Booster power).
Each screen may be opened in one of three modes of operation;
(1) Drafting Mode
(2) Real-time Run Mode
(3) Simulated Run Mode (One can force various parameters and control
equipments for making expert decision based on simulation. The
simulation algorithm generates two types of output, one for the real
module and another for simulation module. Both results are logged to the
database for visualization.)
Although each screen may contain a network graph or a plant layout or a
combination of both, gas-balance module and network-analysis module
run in the background for its own individual entities.
(B) GAS Balance Module
With this module a plant manager can input energy demand along with
various types of energy sources. The system then performs optimization

function and presents optimum fuel combination which satisfies all constraints
entered and produces the desired energy.
(C) Network Simulation Module
With this module a plant manager can perform planning, scheduling,
monitoring and acknowledgement of activities, outage or shutdown
plans, energy or cost commitments. With this module a plant manager
can visualize the energy forecast against a desired production target,
which in turn helps in decision making.
(D) Data Acquisition Module
This function module is responsible for acquiring field data and
populating the data base. The general data is routed through plant
control network covering most of the generation and consumption units.
(E) Network Diagnostics (Reports)
Reports are generated based on the time span input from the user or for
a preprogrammed time period (last month/ last financial year/ last 3
years). The reports are grouped, based on various functions or
categories (By Utility By Plant/ By Section/ By Equipment/ By
Month/Year). The reports are generated in various (Time series
Format/Tabular Format/ and Spider Chart Format).
(F) Energy Audit Module (Reports)
This provides reports for auditing energy performance against a
committed target of auditable energy entities.
• Annual Business Planner Module
• Reporting & MIS Module

(G) Expert Decision Module
This provides a priority list of the consumers, and their requirements.
(H) Crisis Management Module
Crisis management module provides management functions for easier
handling of situations during a crisis. It comprises a list of contact
persons, who are called electronically.
DETAILS OF OPERATION OF THE MODULES:
a) Plant Configuration Module
This is a means to develop and configure a specific energy management
system. This would mean configuration of lay out, items like generators,
consumers and rest of the process modules, and the inter-connecting
pipelines and valves. The rules governing the permissible ranges of
operation and priorities are embedded in this module.
This module is a tool to build a gas network system. The system deals
with standard objects like
■ Generator: A typical generator comprises a Blast Furnace or a BOF
vessel. It can be fully configured by the following variables.
■ Input Variable: These are the key variables that are directly related
to generation of the gas
■ Input Variable Ranges: The engineering scales of the input
variables

■ Status Trigger: These are discrete inputs which define the state or
help in defining the state of the module (generator)
■ Status: Depicts the states of the output variable
■ Output Variable: Usually the Gas generated & Energy Value
■ Transfer Variable: Transfer function is the dynamic relation
between the input variable and the output. This helps in simulating
the output on feeding the input. The transfer function is set by
input states. This comprises of a learning function so that when
the system is configured the system is identified automatically
in a specified time and number of sample data. This depicts the
transient behavior of the generator in the specified states. The
transfer function is defined by the Network Simulation module.
■ Training samples: The number of data point specified for
system identification.
■ Reset function: Resets the transfer function.
■ Auto reset: The function automatically resets the transfer
function
Consumer: A typical consumer is like a Boiler or a Reheating
furnace.
They can be fully configured as previously described (under the heads
input Variable(Steam Flow, Mill rates) + Input Variable Ranges +
Status Trigger(Consumer)+ Status + Output Variable (Usually the Gas
consumed) + Transfer function + Training samples + Reset function
+ Auto reset)
Transfer unit: These are storage and boosting devices and
mixing stations.

They can be fully configured as previously described (under the heads
input Variable(Gas Flow) + Input Variable Ranges +Status
Trigger+ Status + Output Variable (Usually the Gas transferred) +
Transfer function + Training samples + Reset function + Auto reset)
(b) Gas Balance Module
The gas balancing is done by adjusting the consumers like reheating furnaces
and boilers within their operating range. The priority is based in on a preset
decision table, or an operating cost function.
The first part is a mass balance of the generators and consumers. In the case
when the demand exceeds the supply, the consumers are curtailed according to
a pre-defined priority list.
Next the network simulation is executed, to predict the gas pressures at the
consumers. When these pressures are below the minimum acceptable criteria,
replacement strategies are evaluated, such as replacing BF gas with LD gas. The
CV requirements of the consumers are considered during this process. Thus, if
BF gas to a consumer (such as a PHS) which has a lower CV, has to be
compensated with a higher mixing of CO gas.
Network simulation is executed in an iterative manner. The open/close status of
the pipeline valves is changed. The procedure is an exhaustive search. The sum
of squares of the deviations from the target values of the consumers, is worked
out in each iteration. And the valve configuration that provides the least square
error provides the solution.

(c) Network Simulation Module
The basic gas network is configured in the configuration module. However this
module derives the present status of the network based on the real time data.
Say a generator/consumer is shutdown temporarily. The gas supply routing is
modified accordingly by constraining the flow at that node to zero. The data from
the sensors is monitored and parameters are set for the Network simulator
module. This is handled by a "Data Acquisition Module", described subsequently.
The gas flow analysis is performed through a computer program . The inputs
comprise the gases supplied from the generators and the gas requirements at
the consumers. The flows in the pipelines are calculated from the piping
configuration of the pipe lengths, diameters, the minor losses due to bends and
constrictions the valves status. The outputs comprise the individual flow rates in
the pipes, the gas velocities, and pressures.
The module simulates the flow and pressure values at different point (nodes) of
the network for a stated decision for a network condition. The simulator passes
the simulation output through a constraint table to check validity. Based on the
output of the simulate data a decision tree of the control module generates
appropriate decisions for controlling the network valves.
The decision tree forms a part of the Expert Decision Module, described
subsequently.

The results from this module are compared with the target settings of the
consumers (gas quantity, pressure, CV), and a sum of the squares of the
deviations is computed. The deviations carry different weights, thus the CV of
the mixed gas at the HSM can vary in a range from 2900+/- 200 (Kcal/Nm3). If
the CV is below 2700, the weightage is 10 (say). The valve configuration that
provides the least square error provides the solution.
A typical BF gas network has been shown in Fig#3, while Fig#5 shows atypical
CO gas network.
The network simulator provides a complete analysis of the flow conditions in a
pipeline network. Fig#6 shows in a BF network for a specific situation, while
Fig#7 shows the concomitant pressures.
The flow conditions are governed by the input parameters such as the
parameters of the generator, the consumers, the transient status of the gas
holders, the boosters in the lines, and the valve settings.
The valve settings are changed during the exhaustive search procedure, to find
the best configuration. Typically there are 100 configurations, each of which is
referred to as a load case.
The calculation procedure invokes the computer program in. In this procedure,
the flows at the nodes are balanced [modes balance], and the pressures in a

loop are balanced [energy balance], following the principles of Kirchoff's Laws.
The pressures are calculated on the basis of the Darcy Wiesabach equations.
These are empirical equations, based on the dimensionless parameters Reynold's
number and Prandl number. The pressure loss in pipes depends on the
roughness coefficients of individual pipes. The initial values are taken from hand
books, wherein the roughness coefficient is provided in terms of the material and
age of the pipe.
The initial roughness coefficient is tuned with the calibration data mentioned in
plant configuration module [Training samples]. This is because the physical
measurements of pipe diameters in a running plant are difficult. During long
operation, the pipes become clogged with deposits, which cause pressure losses
greater than established norms. This exercise is a one off task. The set of
roughness coefficients that best match the results of the training samples, is
stored in a database. Subsequently, when the network simulation module is
executed, the roughness coefficients from the database are downloaded.
The network simulation module refers to the Transfer function defined in Plant
Configuration Module.
(d) DATA ACQUISITION MODULE:
The data acquisition module assigns data to a tag name. High low alarms are
set by the module for all acquired data.

The various items in the "Data Acquisition Module" are as follows:
d(i) Details of the Level-2 Automation system at blast furnaces
Iron ore lumps and pellets, coke, sinter are charged into the blast furnace. Hot
air is blasted into the bottom of the furnace and the oxygen in the air combusts
with the coke to from carbon monoxide gas. The carbon monoxide flows up
through the blast furnace and removes oxygen from the iron ores on their way
down, thereby leaving iron. The heat in the furnace melts the iron, and the
resulting liquid iron (or hot metal as it is called in the industry). The gases
generated at the blast furnace comprise about half and half of CO and CO2. The
CO component has a calorific value, as it can be combusted to produce heat and
CO2. The calorific value of the BF gas is about 900 [Kcal/NM^3], and is also
referred to as "Lean gas".
The specific BF gas generation is about 1.7 [1000NM3/THM] with an energy
content of 1.53 [Gcal/THM]. Typically about a quarter of the BF gas generation
is consumed in the BF stoves for pre-heating the incoming air [1000degC]. The
fuel to the BF stoves is enriched by the addition of CO gas [4%], to increase the
CV.
The blast furnaces have to be periodically shut down, for essential maintenance
operations. The shut down is achieved by shutting off the air flow to the
furnace. During a shut down, the gas generation as well as the consumption in

the stove is essentially zero. When it comes time to re-start the furnace, the hot
blast stoves are fired for heating the input blast air. The BF gas that is required
is obtained from other operating furnaces, supplemented by the gas stored in
the gas holders. After the stoves are operating, the hot blast is fed into the blast
is fed into the blast furnace, and normal BF gas generation commences [typically
after 20 minutes].
The trend of the gas requirements has been shown in Fig#3. The period of
interest is from 12/04/08 [MM/DD/YY] 18:10:00 To 18:30:00. During this period,
BF gas from the network has to supply the blast furnace stoves, till the internal
generation from the blast furnace picks up. The supply of gases to the BF stove
has to be ensured, not only as regards the quantity [About 5000 NM^3/Hr for a
1MT pa Blast Furnace], but also with regard to the pressure [About 10.4 MWC].
This is in order to meet the requirements of the burners at the Cowper Stoves.
The gas demand during this interval is also not constant, but has to be
anticipated from prior trends. For this purpose a smoothened trend provides a
better estimate.
The gas flows are measured with sensors, and the readings are stored in a
computer data base with a unique identifier [Tag Name: FBFNEW.TGVOLF_CV:
For the BF gas generation at F blast at Tata Steel]. These data are acquired by
the system from the distributed data base spanning all the operating blast
furnaces, as well as from the storage holder.

d(ii) DETAILS FO THE LEVEL-2 AUTOMATION SYSTEM AT COKE OWENS
Blended coal is heated in coke ovens to produce coke by the process called
carbonization. The gas produced during carbonization [CO gas] is used for fuel
elsewhere in the steelworks. About 25% by weight of the coal comprises of
volatile materials. It consists of a mixture of hydro carbons, and has a calorific
value of about 10,000 [For methane= 9464Kcal/NM^3].
The CO gas is a prize fuel. As in the case of BF gas, some of the CO gas is also
used in the coke ovens, for firing the ovens. A mixture of BF gas and CO gas is
used.
A process similar to that for BF gas desired in the previous section is used to
acquire the flow rates, and estimate the gas production rates, as well as to
anticipate the internal demands [Internal to the coke oven plant].
d(iii) DETAILS OF THE LEVEL-2 AUTOMATION SYSTEM AT LD
The hot metal is converted to steel by blowing pure oxygen at high pressure in
the converters at the basic oxygen steelmaking shop. The molten steel form the
converter is further refined at the Ladle Metallurgy facility where the chemistry
and temperature are finely adjusted before casting into slabs and billets. When
oxygen is blown in the BOF converter, the Carbon (4%C) in the Hot metal is
burned to Coat the high temperatures in the converter (1700 degC). Later, when

the fumes cool down, some of the CO is converted to CO2, with the result that
the Calorific value decreases. The specific LD gas generation is approximately
0.081(1000NM3/TLS) with an energy content of 0.166(Gcal/TLS).
Unlike the Blast Furnaces, the Basic Oxygen converter is a batch process, and
the gases are generated during a portion of the time of the complete cycle. This
intermittent gas supply can only be conveniently used if there are storage
facilities. In a typical installation there are 2 LD gas holders, so that even during
simultaneous blowing of 2 BOF converters, the gases can still be stored, and
need not be flared due to paucity of storage capacity. For a 5 MTpa steel plant,
the LD gas recovery is about 46(1000NM^3/Hr). The LD gas from the recovery
system is mixed with Coke Ovens Gas for utilization in the steel works.
A slightly modified process as that described for BF gas is used to acquire the
flow rates, and estimate the gas production rates, as well as to anticipate the
internal demands (Internal to the BOF Plant). The LD gas storage tanks are
modeled in the system, and the transient flow limits are incorporated.
d(iv) Details of the level-2 Automation system at PHS
Integrated steel plants have captive electrical generators. Generally they use
steam from coal fired boilers to run turbines coupled to alternators. The
electricity is commonly connected to the local electrical utility supply grid. The
waste gases in the steel plant can be gainfully used by converting coal fired
boilers in the Power House to by-product gas fired boilers (BF gas, CO gas & LD
gas), there by reducing the boiler coal consumption in the plant. The surplus

Blast Furnace gas being generated can be gainfully used, instead of being flared
when there is no consumer for the lean gas. In a typical steel plant (Tata Steel),
the heat input to boilers through coal has reduced significantly (from 49% in
2001 to 26% in 2007. Two Boilers (5&6) at PH#3 were converted from Coal
Firing to Gas Firing in 2006 so that the consumption of boiler coal in these boilers
has become zero and has utilized the 1,10,000 Nm3/hr BF Gas, which was earlier
being flared. The specific energy consumption has also reduced by 0.072
Gcal/tons of steam).
The electrical supply from the public utility grid is subject to outage. In such
circumstances, the essential services in the steel plant must be maintained (Blast
Furnace blowers, Coke Oven pusher cars etc). The minimum gas requirements,
pressures, CV are estimated from the trends, as described earlier. These inputs
form a part of the Consumer requirements, in the Einergy balance.
d(v) Details of the level-2 Automation system at HSM
Integrated steel plants have rolling mills, where the cast steel slabs are rolled to
sheets. This is done in two stages, HSM and Cold Rolling Mills. In the HSM, the
steel slabs are heated to 1300 degC, and then are passed through rolls. The
heating operation is the most energy intensive operation in the steel plant. In a
typical Hot Strip Mill there are two walking beam type 350 tons per hour
reheating furnaces. The fuel to the furnace is mixed gas of CV 2900 Kcal/Nm3
and air to the reheating furnace is supplied through four air blowers of 75,000
Nm3/hr capacities.

The gas requirements (BF and CO gas ), pressures, CV are estimated from the
trends, as described earlier. These inputs form a part of the Consumer
requirements, in the Energy balance.
Similarly for the Merchant Mill and the Wire Rod Mill.
(e) Network Diagnostics (Reports)
Apart from getting the actual energy consumption volume and its cost data in a
standard report, one is able to add additional fields for volume and cost of
following entities for improved visualization which are: Input Energy + Energy
Loss+ Energy Recycled+ Utility Inputs+ Utility By-Products+ Utility Outputs+
Production.
(f) Energy Audit Module (Reports)
Reports are generated based on per-programmed time period that is for example
last financial year, last quarter. Audit report presents actual data against the
committed data for following items.
Utility Input+ Input Energy + Effluent Output+ Energy Consumed Per Unit: of
Production (Efficiency)+ Shutdown Requirements vs. Actual
(Commitment information has to be entered by suitable agency at the time of
audit report generation)
A sample has been shown in Table#1.

(g) Expert Decision Module
This is a table of information that is configured according to the individual plant
setup. IT provides a priority list of the consumers, and their requirements.
Thus a Blast Furnace stove may be assigned a priority weight of 100, and in that
the BF gas requirement of a stove (63000+/-1000 NM^3Hr) may have a
weightage of 0,(60000-62000) may have a weight 0,(10-10.2MWC) weight
20000. Then, if the network simulation for a particular state of valve status,
provides a 61000 NM^3 flow at a pressure of 10.1MWC,the contribution to the
error function becomes 100*(61000-63000^2*0.01+(10.1-
10.4)^2*2000)=4018000
The penalty or error function is computed by summing over all the consumers for
a particular load case (Valve status set). The one with the lowest error function
is selected.
(h) Crisis Management Module
This module comprises a list of contact persons, who are called electronically.
The data is structured in a tree format. The specific area of crisis, determines the
branch on the tree where the calls are directed.
Functions provided by this module is as follows:
1. Activity Manager: He manages activities(both scheduled
and unscheduled),which is supposed to be carried out
during any emergency situation. It helps in quick location
of important information about crisis handling activities
like-operating procedures, relevant drawings, contact
persons, current status, deadline, priorities etc.

2. Drawings Manager: He provides functionality for
managing important plant drawings, manuals etc in a
central database. In the event of crisis these drawings,
manuals etc. can be rapidly accessed or emailed to the
desired persons.
3. Contacts Manager : He provides contact management
functionality along with email, SMS, and paging services.
Also reflects details of responsibilities associated with a
particular person like crisis activity, drawing.
4. Trouble Call Logging: Provides functionality for managing
(logging , handling, auto-call logging, auto-call dispatch,
escalating etc.) of calls made during a crisis situation by
system users typically they are customers.
5. Crew Manager: Provides functionality for work-order
management during crisis situations, includes automated
crew selection.

WE CLAIM:
1. A system for optimization of energy through control of gas follows in
pipeline network in a steel plant comprising of :
- plant configuration module;
- gas balance module;
- network simulation module;
- data acquisition module;
- network diagnostics module;
- energy audit module;
- expert decision module and
- crisis management module,
characterized in that the system optimizes gas flow in the pipelines on the
basis of derived data for generators and consumers of steelplant gases in
consideration of different gas storages.
2. A system as claimed in claim 1, wherein the said plant configuration
module modifies the pipeline network based on simulation in conjunction
with said gas balance module.
3. A system as claimed in claim 1, wherein said gas balance module ensures
optimum balance between energy demand and energy sources through
mass balance and network simulation.
4. A system as claimed in claim 1, wherein said network simulation module
visualizes energy forecast against desired production target through
planning, scheduling and monitoring.

5. A system as claimed in claim 1, wherein said data acquisition module
performs the task of acquiring and routing field data through plant control
network.
6. A system as claimed in claim 1, wherein said network diagnostics module
generates a plurality of reports.
7. A system as claimed in claim 1, wherein said energy audit module
generates a plurality of reports for auditing energy performance against
committed targets for auditable energy entities.
8. A system as claimed in claim 1, wherein said expert decision module
provides a priority list of consumers and their requirements.
9. A system as claimed in claim 1, wherein said crisis management module
provides input to the management in times of crisis.

A system for optimization of energy through control of gas follows in pipeline
network in a steel plant comprising of plant configuration module;
gas balance module; network simulation module; data acquisition module;
network diagnostics module; energy audit module;
expert decision module and crisis management module,
characterized in that the system optimizes gas flow in the pipelines on the basis derived data for generators and consumers of steelplant gases in consideration of different gas storages.